1200 Free Computer Science Courses from the World’s Top Universities

In this article, we’ve compiled 1200+ online courses offered by the 60 best universities in the world for studying computer science in 2024.

We first built the list in 2020 using a data-driven approach that we have used each year, including 2024. You can find the methodology below.

Methodology

First, we identified the leading world university rankings. Since we were interested in computer science specifically, he looked at their latest computer science rankings.

For the 2024 update, Suparn, my colleague used the same sources and methodology to find the top 60 universities offering online computer science courses.

Here are the rankings used:

• QS: World University Ranking 2024 — Computer Science & IT

• Times Higher Education: World University Ranking 2024 — Computer Science

• Shanghai Ranking: Academic Ranking of World Universities 2023 — Computer Science & Engineering

Then, we crawled and scraped each ranking.

Now that we had some data, we used Jupyter with Python to process it. We combined the three rankings into one by averaging the position of each university in each ranking. Then, we filtered out the universities that didn’t offer online courses, and limited the list to the top 60 institutions — the cream of the crop.

Combined ranking: top 10 universities for studying computer science in 2024

As you can see above, we found that the top three institutions are #1 MIT, #2 Stanford, and #3 Carnegie Mellon.

Finally, we used the Class Central database, with its 250K online courses, to find all the computer science courses offered by the universities in the ranking.

The end result is a list of 1200+ online courses offered by the 60 best universities in the world for studying computer science in 2024.

Stats

• Enrollments range from 10 to over 13 million, with 20 courses exceeding 1 million enrollments

• Altogether, they have over 110M enrollments, with an average of 155K enrollments

• 1180 courses are in English, 39 Chinese, 15 Spanish, 13 Arabic, 12 French, 12 Korean, 7 Russian, 3 Portuguese, 3 German, 1 Dutch, and 1 Japanese

• Together, they account for more than 78K reviews at Class Central, with an average of 216 reviews

• Average rating: 4.07 out of 5.0

• All these courses are free or can be audited for free

• 240 courses are beginner level, 281 are intermediate level, and 70 are advanced level.

More Courses

The full list is split into subjects. Click on a subject below to go to the relevant section. With over 1200 courses to pick from, I hope you find something you like. But if these aren’t enough, check out Class Central’s catalog of 250K online courses or our thematic collections:

• 900+ Free Developer and IT Certifications

• Harvard Computer Science Courses with Free Certificate

• 40+ Free Certificates from Wolfram U

• 10,000+ Free Courses from Tech Giants: Learn from Google, Microsoft, Amazon, and More

• 8000+ OpenCourseWare Courses from Top Institutions.

Programming (267)

• Programming for Everybody (Getting Started with Python) from University of Michigan ★★★★★(44479)

• Python Data Structures from University of Michigan ★★★★★(16763)

• Using Python to Access Web Data from University of Michigan ★★★★★(5744)

• Using Databases with Python from University of Michigan ★★★★★(4709)

• Python and Statistics for Financial Analysis from The Hong Kong University of Science and Technology ★★★★☆(573)

• Computing in Python I: Fundamentals and Procedural Programming from Georgia Institute of Technology ★★★★★(254)

• CS106B, Programming Abstraction in C++ from Stanford University ★★★★★(164)

• Learn to Program: The Fundamentals from University of Toronto ★★★★★(110)

• Computing in Python IV: Objects & Algorithms from Georgia Institute of Technology ★★★★★(107)

• Data Science and Agile Systems for Product Management from University System of Maryland ★★★★★(97)

• Computing in Python II: Control Structures from Georgia Institute of Technology ★★★★★(59)

• Introduction to HTML5 from University of Michigan ★★★★☆(52)

• Computing in Python III: Data Structures from Georgia Institute of Technology ★★★★★(48)

• Programming Mobile Applications for Android Handheld Systems: Part 1 from University of Maryland, College Park ★★★★☆(41)

• Functional Program Design in Scala from École Polytechnique Fédérale de Lausanne ★★★★★(40)

• Introduction to Programming for the Visual Arts with p5.js from University of California, Los Angeles ★★★★★(36)

• Programming Languages, Part A from University of Washington ★★★★★(27)

• CS50's Web Programming with Python and JavaScript from Harvard University ★★★★★(26)

• Database Systems - Cornell University Course (SQL, NoSQL, Large-Scale Data Analysis) from Cornell University ★★★★★(24)

• HTML, CSS, and Javascript for Web Developers from Johns Hopkins University ★★★★★(20)

• Object Oriented Programming in Java from University of California, San Diego ★★★★☆(16)

• Programming Mobile Applications for Android Handheld Systems: Part 2 from University of Maryland, College Park ★★★★☆(15)

• Introduction to CSS3 from University of Michigan ★★★★★(14)

• Python Data Structures from University of Michigan ★★★★★(12)

• Interactivity with JavaScript from University of Michigan ★★★★☆(12)

• Using Python for Research from Harvard University ★★★★☆(12)

• Code Yourself! An Introduction to Programming from University of Edinburgh ★★★★☆(11)

• CS50's Introduction to Programming with Python from Harvard University ★★★★★(11)

• Learn to Program: Crafting Quality Code from University of Toronto ★★★★☆(10)

• Programming for Everybody (Getting Started with Python) from University of Michigan ★★★★★(9)

• Advanced Styling with Responsive Design from University of Michigan ★★★★☆(8)

• MATLAB and Octave for Beginners from École Polytechnique Fédérale de Lausanne ★★★★☆(8)

• Introduction To Swift Programming from University of Toronto ★☆☆☆☆(7)

• Introduction to Java Programming – Part 1 from The Hong Kong University of Science and Technology ★★★☆☆(6)

• CS50's Introduction to Programming with Scratch from Harvard University ★★★★☆(6)

• Parallel programming from École Polytechnique Fédérale de Lausanne ★★★★☆(6)

• Creating Video Games from Massachusetts Institute of Technology ★★★★☆(6)

• Capstone: Retrieving, Processing, and Visualizing Data with Python from University of Michigan ★★★☆☆(6)

• Single Page Web Applications with AngularJS from Johns Hopkins University ★★★★★(5)

• The Computing Technology Inside Your Smartphone from Cornell University ★★★★★(5)

• Web Coding Fundamentals: HTML, CSS and Javascript from National University of Singapore ★★★★★(5)

• Software Development Process from Georgia Institute of Technology ★★★★☆(5)

• Introduction To MATLAB Programming (Fall 2011) from Massachusetts Institute of Technology ★★★★★(5)

• CS 193a: Android App Development Winter 2019 from Stanford University ★★★★★(5)

• Introduction à la programmation orientée objet (en C++) from École Polytechnique Fédérale de Lausanne ★★★★☆(4)

• Building Web Applications in PHP from University of Michigan ★★★★★(3)

• Introduction to Structured Query Language (SQL) from University of Michigan ★★★★☆(3)

• Understanding and Visualizing Data with Python from University of Michigan ★★★★★(3)

• Databases: Relational Databases and SQL from Stanford University ★★★★☆(3)

• Mastering the Software Engineering Interview from University of California, San Diego ★★★★☆(3)

• Initiation à la programmation (en C++) from École Polytechnique Fédérale de Lausanne ★★★★★(3)

• iOS App Development Basics from University of Toronto ★★★★☆(2)

• CS50's Mobile App Development with React Native from Harvard University ★★★★★(2)

• Software Engineering Essentials from Technische Universität München (Technical University of Munich) ★★★★☆(2)

• App Design and Development for iOS from University of Toronto ★★★☆☆(2)

• Python Basics from University of Michigan ★★★★★(2)

• CS50's Introduction to Databases with SQL from Harvard University ★★★★☆(2)

• التفاعل مع لغة البرمجة جافا سكريبت from University of Michigan ★★★☆☆(2)

• R البرمجة باستخدام لغة from Johns Hopkins University ★★★★☆(2)

• Programmation pour tous (mise en route de Python) from University of Michigan ★★★★☆(2)

• Make Your Own App from Technische Universität München (Technical University of Munich) ★★★★★(1)

• Building Database Applications in PHP from University of Michigan ★★★★★(1)

• Database Design and Basic SQL in PostgreSQL from University of Michigan ★★★★★(1)

• JSON and Natural Language Processing in PostgreSQL from University of Michigan ★★★★★(1)

• Database Systems Concepts & Design from Georgia Institute of Technology ★★★★☆(1)

• Building Web Applications in Django from University of Michigan ★★★★★(1)

• Automated Software Testing: Model and State-based Testing from Delft University of Technology ★★★★★(1)

• Fitting Statistical Models to Data with Python from University of Michigan ★★★★★(1)

• Web Design for Everybody Capstone from University of Michigan ★★★★☆(1)

• Inferential Statistical Analysis with Python from University of Michigan ★★★★★(1)

• Lernen objekt-orientierter Programmierung from Technische Universität München (Technical University of Munich) ★★★★★(1)

• Python Functions, Files, and Dictionaries from University of Michigan ★★★★★(1)

• Programming with Scratch from The Hong Kong University of Science and Technology ★★☆☆☆(1)

• ¡A Programar! Una introducción a la programación from University of Edinburgh ★★★★★(1)

• البرمجة للجميع - بدء استخدام بايثون from University of Michigan ★★★★★(1)

• Initiation à la programmation (en Java) from École Polytechnique Fédérale de Lausanne ★★★★★(1)

• Introduction à la programmation orientée objet (en Java) from École Polytechnique Fédérale de Lausanne ★★★★★(1)

• Programación para todos (empezando con Python) from University of Michigan ★★★★☆(1)

• 计算导论与C语言基础 from Peking University ★★★★☆(1)

• Stanford Seminar - Optional Static Typing for Python from Stanford University ★★★★★(1)

• Программирование для всех (начало работы с Python) from University of Michigan ★★★★★(1)

• 15-721 Advanced Database Systems (Spring 2017) from Carnegie Mellon University ★★★★☆(1)

• 15-721 Advanced Database Systems (Spring 2019) from Carnegie Mellon University ★★★★★(1)

• Seven Databases in Seven Weeks (Fall 2014) from Carnegie Mellon University ★★★★★(1)

• Stanford Webinar - Cloud Computing: What’s on the Horizon with Dr. Timothy Chou from Stanford University ★★★☆☆(1)

• JavaScript, jQuery, and JSON from University of Michigan

• Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability from Delft University of Technology

• Quantitative Methods for Biology from Harvard University

• Build Your Own iOS App from University of Toronto

• Software Engineering: Implementation and Testing from The Hong Kong University of Science and Technology

• Introduction to Python Programming from University of Pennsylvania

• Databases: Advanced Topics in SQL from Stanford University

• Introduction to Java Programming – Part 2 from The Hong Kong University of Science and Technology

• Software Analysis & Testing from Georgia Institute of Technology

• Introduction to Java and Object-Oriented Programming from University of Pennsylvania

• Using JavaScript, JQuery, and JSON in Django from University of Michigan

• Software Engineering: Software Design and Project Management from The Hong Kong University of Science and Technology

• Database Design and Basic SQL in PostgreSQL from University of Michigan

• Creating Virtual Reality (VR) Apps from University of California, San Diego

• R Programming Fundamentals from Stanford University

• Programming in C from University of Michigan

• Programming Languages Ⅰ from Korea Advanced Institute of Science and Technology

• Data Structures in C from University of Michigan

• Big Ideas in Programming: Expressing Yourself with Python from University of Michigan

• Software Engineering: Modeling Software Systems using UML from The Hong Kong University of Science and Technology

• Web App Development with the Power of Node.js from Technische Universität München (Technical University of Munich)

• Developing Android Apps with App Inventor from The Hong Kong University of Science and Technology

• Web Application Technologies and Django from University of Michigan

• Using JavaScript and JSON in Django from University of Michigan

• Data Collection and Processing with Python from University of Michigan

• UML Class Diagrams for Software Engineering from KU Leuven University

• Building Objects in C from University of Michigan

• Mobile Application Experiences from Massachusetts Institute of Technology

• How Virtual Reality Works from University of California, San Diego

• Programming Languages Ⅱ from Korea Advanced Institute of Science and Technology

• The Power of Object-Oriented Programming from University of Michigan

• Intermediate PostgreSQL from University of Michigan

• CS50's Introduction to Programming with R from Harvard University

• Answering Interesting Questions with Data from University of Michigan

• Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan

• Inheritance and Data Structures in Java from University of Pennsylvania

• Databases: Modeling and Theory from Stanford University

• 3D Graphics in Android: Sensors and VR from Imperial College London

• Exploring C from University of Michigan

• Introduction to Neurohacking In R from Johns Hopkins University

• Using JavaScript and JSON in Django from University of Michigan

• Introduction to Android graphics from Imperial College London

• Introduction to Object-Oriented Programming with Java III: Exceptions, Data Structures, Recursion, and GUIs from Georgia Institute of Technology

• Worldbuilding for Video Games from The University of British Columbia

• JSON and Natural Language Processing in PostgreSQL from University of Michigan

• Statistical Learning with Python from Stanford University

• Real-Time Audio Signal Processing in Faust from Stanford University

• Intermediate PostgreSQL from University of Michigan

• Introduction to Javascript and Ajax: Building Web Apps from Johns Hopkins University

• Cloud Applications from Georgia Institute of Technology

• Databases: Semistructured Data from Stanford University

• Introduction to Object-Oriented Programming with Java I: Foundations and Syntax Basics from Georgia Institute of Technology

• Global Software Development from Delft University of Technology

• Advanced App Development in Android Capstone from Imperial College London

• Practical Python for AI Coding 1 from Korea Advanced Institute of Science and Technology

• Debugging: Hunting and Squashing Bugs from University of Michigan

• Intro to AR/VR/MR/XR: Technologies, Applications & Issues from University of Michigan

• Introduction to Parallel Programming with CUDA from Johns Hopkins University

• Introduction to Object-Oriented Programming with Java II: Object-Oriented Programming and Algorithms from Georgia Institute of Technology

• Databases: OLAP and Recursion from Stanford University

• Minecraft, Coding and Teaching from University of California, San Diego

• Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan

• Cloud Systems Software from Georgia Institute of Technology

• Introduction à la science des données sociales avec R from Université de Montréal

• Practical Python for AI Coding 2 from Korea Advanced Institute of Science and Technology

• Programming Reactive Systems from École Polytechnique Fédérale de Lausanne

• Projet de programmation (en Java) from École Polytechnique Fédérale de Lausanne

• Getting Started with Data Visualization in R from Johns Hopkins University

• Cloud Computing Project from University of Illinois at Urbana-Champaign

• Python Classes and Inheritance from University of Michigan

• Advanced Data Visualization with R from Johns Hopkins University

• Django Features and Libraries from University of Michigan

• Programação para todos (Conceitos básicos de Python) from University of Michigan

• Effective Programming in Scala from École Polytechnique Fédérale de Lausanne

• Building Web Applications in Django from University of Michigan

• Developing AR/VR/MR/XR Apps with WebXR, Unity & Unreal from University of Michigan

• Data Visualization in R with ggplot2 from Johns Hopkins University

• Programming Reactive Systems (Scala 2 version) from École Polytechnique Fédérale de Lausanne

• Web Application Technologies and Django from University of Michigan

• Coding the Static Restaurant Site from Johns Hopkins University

• Importing Data in the Tidyverse from Johns Hopkins University

• Functional Programming Principles in Scala (Scala 2 version) from École Polytechnique Fédérale de Lausanne

• Visualizing Data in the Tidyverse from Johns Hopkins University

• CUDA Advanced Libraries from Johns Hopkins University

• Functional Program Design in Scala (Scala 2 version) from École Polytechnique Fédérale de Lausanne

• 面向对象技术高级课程（The Advanced Object-Oriented Technology） from Peking University

• Modeling Data in the Tidyverse from Johns Hopkins University

• Introduction to the Tidyverse from Johns Hopkins University

• CUDA at Scale for the Enterprise from Johns Hopkins University

• Introduction to Internationalization and Localization from University of Washington

• Programming Reactive Systems from École Polytechnique Fédérale de Lausanne

• Parallel programming (Scala 2 version) from École Polytechnique Fédérale de Lausanne

• Publishing Visualizations in R with Shiny and flexdashboard from Johns Hopkins University

• Wrangling Data in the Tidyverse from Johns Hopkins University

• مقدمة عن لغة HTML5 from University of Michigan

• Django Features and Libraries from University of Michigan

• Functional Programming in Scala Capstone from École Polytechnique Fédérale de Lausanne

• Estruturas de dados Python from University of Michigan

• Uso de bancos de dados com Python from University of Michigan

• MATLAB et Octave pour débutants from École Polytechnique Fédérale de Lausanne

• C++程序设计 from Peking University

• Estructuras de Datos con Python from University of Michigan

• Introducción al HTML5 from University of Michigan

• Java程序设计 from Peking University

• Programación para todos (Introducción a Python) from University of Michigan

• مقدمة عن CSS3 from University of Michigan

• Einführung in MATLAB from Technische Universität München (Technical University of Munich)

• C#程序设计 from Peking University

• Python بُنى بيانات from University of Michigan

• برمج بنفسك! مقدمة حول البرمجة from University of Edinburgh

• Virtual Reality from University of Illinois at Urbana-Champaign

• Uso de bases de datos con Python from University of Michigan

• تنميط متقدم بتصميم سريع الاستجابة from University of Michigan

• 程序设计基础 from Peking University

• Python استخدام قواعد البيانات مع from University of Michigan

• Введение в HTML5 from University of Michigan

• Uso de Python para Acceder a Datos Web from University of Michigan

• CS193p - Developing Apps for iOS from Stanford University

• CS193p iPhone Application Development Spring 2020 from Stanford University

• Estructuras de datos de Python from University of Michigan

• C程序设计进阶 from Peking University

• Stanford Seminar - Extended Reality for Everybody, Michael Nebeling from Stanford University

• Stanford Seminar-Stories from CoCoLab: Probabilistic Programs, Cognative Modeling, & Smart Web Pages from Stanford University

• 游戏策划与设计 from Fudan University

• 基于Unity引擎的游戏开发基础 from Fudan University

• Stanford Seminar - Accessible Virtual Reality for People with Limited Mobility from Stanford University

• Stanford Seminar - How to Design Addictive Games from Stanford University

• CS 241: System Programming from University of Illinois at Urbana-Champaign

• Learn Computer Science Online from University of Illinois at Urbana-Champaign

• Haskell: Lecture notes and assignments from University of Pennsylvania

• 15-445/645 Intro to Database Systems (Fall 2017) from Carnegie Mellon University

• 15-721 Advanced Database Systems (Spring 2016) from Carnegie Mellon University

• 游戏产业概论 from Fudan University

• 网络游戏设计与开发毕业项目 from Fudan University

• Stanford Seminar - Programing Should Be More Than Coding from Stanford University

• Stanford Seminar - Concatenative Programming: From Ivory to Metal from Stanford University

• Работа с базами данных в Python from University of Michigan

• Programming languages the fundamental tools of the computer age from University of Melbourne

• Ciencia de Datos: Fundamentos de R from Harvard University

• 15-721 Advanced Database Systems (Spring 2020) from Carnegie Mellon University

• 15-445/645 Intro to Database Systems (Fall 2018) from Carnegie Mellon University

• 15-721 Advanced Database Systems (Spring 2018) from Carnegie Mellon University

• 软件工程 from Peking University

• Stanford Seminar - Mind Your State for Your State of Mind from Stanford University

• Использование языка Python для доступа к веб-данным from University of Michigan

• Python에서 데이터베이스 사용하기 from University of Michigan

• Missing Semester IAP 2020 from Massachusetts Institute of Technology

• 15-445/645 Intro to Database Systems (Fall 2021) from Carnegie Mellon University

• Hardware Accelerated Database Lectures (Fall 2018) from Carnegie Mellon University

• Time Series Database Lectures (Fall 2017) from Carnegie Mellon University

• The Databaseology Lectures (Fall 2015) from Carnegie Mellon University

• Stanford Seminar - Making Teamwork an Objective Discipline - Sid Sijbrandij CEO & Chairman of GitLab from Stanford University

• Stanford Seminar - KUtrace 2020 from Stanford University

• Stanford Seminar - Wearing a VR Headset While Driving to Improve Vehicle Safety from Stanford University

• Структуры данных Python from University of Michigan

• R 프로그래밍 from Johns Hopkins University

• Structure and Interpretation of Computer Programs from Massachusetts Institute of Technology

• 15-445/645 Intro to Database Systems (Fall 2019) from Carnegie Mellon University

• PACS & AI – From Integration to Cloud from Yale University

• VR/AR in IR: Mixed Reality in Medicine from Yale University

• 基于Unity引擎的游戏开发进阶 from Fudan University

• Jeremy Bailenson: Your Mind on the Metaverse from Stanford University

• Stanford Seminar - #TechFail: From Intersectional (In)Accessibility to Inclusive Design from Stanford University

• Stanford Seminar - Understanding the Utility of Haptic Feedback in Telerobotic Devices from Stanford University

• Stanford Seminar: I forgot, I invented hypertext - Ted Nelson from Stanford University

• Stanford Seminar - Making the Invisible Visible: Observing Complex Software Dynamics from Stanford University

• Stanford Seminar - Edge Computing and the Evolution of AR/VR (panel discussion) from Stanford University

• Stanford Seminar - The Future of Edge Computing from an International Perspective from Stanford University

• Stanford Seminar: Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems from Stanford University

• Stanford Seminar - Robosion: Software Platform for Lifelike Humanoids from Stanford University

• Stanford Seminar - Rocket: Securing the Web at Compile-time from Stanford University

• Stanford Seminar - Graph Analysis of Russian Twitter Trolls Using Neo4j from Stanford University

• Stanford Seminar - An Alternative to the American way of Innovation from Stanford University

• Stanford Seminar: PyWren - Pushing Microservices to Teraflops from Stanford University

• Stanford Seminar: Living in Information Everywhere from Stanford University

• Stanford Seminar - From Flat to Phantasmal from Stanford University

• Computer Language Engineering (SMA 5502) from Massachusetts Institute of Technology

• Internet Technology in Local and Global Communities from Massachusetts Institute of Technology

• 빅 데이터 모델링 및 관리 시스템 from University of California, San Diego

• 17. Paul's Disciples from Yale University

• LaTeX course from University of Amsterdam

• JAVA程序设计进阶 from Tsinghua University

Computer Science (736)

• Information Systems Auditing, Controls and Assurance from The Hong Kong University of Science and Technology ★★★★★(686)

• Harvard CS50 – Full Computer Science University Course from Harvard University ★★★★★(615)

• CS50's Introduction to Computer Science from Harvard University ★★★★★(183)

• Introduction to Computer Science and Programming Using Python from Massachusetts Institute of Technology ★★★★☆(130)

• Artificial Intelligence from Massachusetts Institute of Technology ★★★★★(76)

• Divide and Conquer, Sorting and Searching, and Randomized Algorithms from Stanford University ★★★★★(68)

• Functional Programming Principles in Scala from École Polytechnique Fédérale de Lausanne ★★★★★(65)

• Algorithms, Part I from Princeton University ★★★★☆(62)

• Cryptography I from Stanford University ★★★★★(53)

• Internet History, Technology, and Security from University of Michigan ★★★★★(41)

• Introduction to Electrical Engineering and Computer Science I from Massachusetts Institute of Technology ★★★★★(41)

• Machine Learning Foundations: A Case Study Approach from University of Washington ★★★★☆(40)

• Introduction to Artificial Intelligence from Stanford University ★★★★☆(31)

• Machine Learning with Python: from Linear Models to Deep Learning. from Massachusetts Institute of Technology ★★★☆☆(28)

• Practical Machine Learning from Johns Hopkins University ★★★☆☆(27)

• Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues from Georgia Institute of Technology ★★★★★(25)

• Algorithmic Toolbox from University of California, San Diego ★★★★☆(23)

• CS50's Introduction to Artificial Intelligence with Python from Harvard University ★★★★★(22)

• Algorithms, Part II from Princeton University ★★★★★(21)

• Cloud Computing Concepts, Part 1 from University of Illinois at Urbana-Champaign ★★★☆☆(21)

• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★★(21)

• Machine Learning: Regression from University of Washington ★★★★★(20)

• Introduction to Machine Learning Course from Stanford University ★★★★☆(20)

• Introduction to Logic from Stanford University ★★★☆☆(20)

• Automata Theory from Stanford University ★★★★☆(20)

• Bitcoin and Cryptocurrency Technologies from Princeton University ★★★★★(19)

• Computer Science 101 from Stanford University ★★★★☆(19)

• CS50's Computer Science for Business Professionals from Harvard University ★★★★★(18)

• Probabilistic Graphical Models 1: Representation from Stanford University ★★★★☆(18)

• Cryptocurrency Engineering and Design from Massachusetts Institute of Technology ★★★★★(18)

• Neural Networks and Deep Learning from DeepLearning.AI ★★★★★(16)

• Data Structures from University of California, San Diego ★★★★☆(16)

• CS50's Understanding Technology from Harvard University ★★★★☆(15)

• How to Code: Simple Data from The University of British Columbia ★★★★☆(15)

• Design of Computer Programs from Stanford University ★★★★☆(14)

• Machine Learning With Big Data from University of California, San Diego ★★☆☆☆(14)

• Unix Tools: Data, Software and Production Engineering from Delft University of Technology ★★★★★(13)

• Human-Computer Interaction I: Fundamentals & Design Principles from Georgia Institute of Technology ★★★★★(13)

• Text Retrieval and Search Engines from University of Illinois at Urbana-Champaign ★★★☆☆(13)

• Discrete Optimization from University of Melbourne ★★★★☆(12)

• Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★★(12)

• Learning from Data (Introductory Machine Learning course) from California Institute of Technology ★★★★★(10)

• Machine Learning: Classification from University of Washington ★★★★★(9)

• Mathematics for Machine Learning: Multivariate Calculus from Imperial College London ★★★★★(9)

• Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure from University of Illinois at Urbana-Champaign ★★★☆☆(9)

• Hardware Security from University of Maryland, College Park ★★★☆☆(9)

• Convolutional Neural Networks from DeepLearning.AI ★★★★★(8)

• CS50's Computer Science for Lawyers from Harvard University ★★★★★(8)

• Software Defined Networking from Georgia Institute of Technology ★★★★☆(8)

• Information and Communication Technology (ICT) Accessibility from Georgia Institute of Technology ★★★★☆(8)

• Reinforcement Learning from Brown University ★★★☆☆(8)

• Usable Security from University of Maryland, College Park ★★★☆☆(8)

• Machine Learning Fundamentals from University of California, San Diego ★★★★☆(8)

• Machine Learning from Georgia Institute of Technology ★★★★☆(7)

• Introduction to Computer Vision from Georgia Institute of Technology ★★★★★(7)

• Cryptography from University of Maryland, College Park ★★★★☆(7)

• Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps from Georgia Institute of Technology ★★★★★(7)

• Interactive Computer Graphics from University of Tokyo ★★★☆☆(7)

• Guided Tour of Machine Learning in Finance from New York University (NYU) ★☆☆☆☆(7)

• Performance Engineering of Software Systems from Massachusetts Institute of Technology ★★★★★(7)

• Advanced Operating Systems from Georgia Institute of Technology ★★★★★(6)

• Introduction to Computer Architecture from Carnegie Mellon University ★★★★★(6)

• Cloud Computing Concepts: Part 2 from University of Illinois at Urbana-Champaign ★★★★★(6)

• Computer Graphics from University of California, San Diego ★★★★☆(6)

• Analysis of Algorithms from Princeton University ★★★★☆(6)

• Data Structures and Performance from University of California, San Diego ★★★★☆(6)

• Computer Networking from Georgia Institute of Technology ★★★★☆(6)

• Structuring Machine Learning Projects from DeepLearning.AI ★★★★☆(6)

• Computer Architecture from Princeton University ★★★★☆(6)

• Applied Machine Learning in Python from University of Michigan ★★★★☆(6)

• Internet of Things: How did we get here? from University of California, San Diego ★★☆☆☆(6)

• Machine Learning for Healthcare from Massachusetts Institute of Technology ★★★★★(6)

• Data Structures: An Active Learning Approach from University of California, San Diego ★★★★★(5)

• Machine Learning: Clustering & Retrieval from University of Washington ★★★★★(5)

• How to Code: Complex Data from The University of British Columbia ★★★★★(5)

• Cloud Networking from University of Illinois at Urbana-Champaign ★★★★☆(5)

• Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms from Georgia Institute of Technology ★★★★★(5)

• Internet of Things: Setting Up Your DragonBoard™ Development Platform from University of California, San Diego ★★★☆☆(5)

• Introduction to Computer Science and Programming in Python from Massachusetts Institute of Technology ★★★★★(5)

• Convolutional Neural Networks for Visual Recognition (Spring 2017) from Stanford University ★★★★☆(5)

• AI for Clinical Trials and Precision Medicine | Ruishan Liu from Stanford University ★★★★★(5)

• Electrical and Computer Engineering - ECE 252 from University of Waterloo ★★★★☆(5)

• Sequence Models from DeepLearning.AI ★★★★★(4)

• Human-Computer Interaction II: Cognition, Context & Culture from Georgia Institute of Technology ★★★★★(4)

• Programming Languages, Part B from University of Washington ★★★★☆(4)

• The Unix Workbench from Johns Hopkins University ★★★★★(4)

• Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms from Georgia Institute of Technology ★★★★★(4)

• 6.S191: Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★☆(4)

• Algorithms on Strings from University of California, San Diego ★★★☆☆(4)

• MIT 6.824 Distributed Systems (Spring 2020) from Massachusetts Institute of Technology ★★★★★(4)

• Distributed Systems from University of Cambridge ★★★★★(4)

• Language, Proof and Logic from Stanford University ★★★★☆(3)

• Software Architecture & Design from Georgia Institute of Technology ★★★★★(3)

• Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from DeepLearning.AI ★★★★★(3)

• Machine Learning: Unsupervised Learning from Brown University ★★★☆☆(3)

• Leading Change in Health Informatics from Johns Hopkins University ★★★★★(3)

• Robotics: Perception from University of Pennsylvania ★★★☆☆(3)

• Internet of Things: Communication Technologies from University of California, San Diego ★★★☆☆(3)

• Object-Oriented Data Structures in C++ from University of Illinois at Urbana-Champaign ★★★★☆(3)

• Probabilistic Graphical Models 2: Inference from Stanford University ★★★★☆(3)

• Algorithms on Graphs from University of California, San Diego ★★★★☆(3)

• Mathematics for Machine Learning: PCA from Imperial College London ★★☆☆☆(3)

• Algorithmic Design and Techniques from University of California, San Diego ★★★★☆(3)

• Advanced Algorithms (COMPSCI 224) from Harvard University ★★★★★(3)

• Stanford Webinar - Using Electronic Health Records for Better Care from Stanford University ★★★★★(3)

• Stanford Seminar - Practical Blockchain Applications - Steven Pu from Stanford University ★★★★★(3)

• Stanford Seminar - Security and the Software Defined Network from Stanford University ★★★★☆(3)

• Introduction to Operating Systems from Georgia Institute of Technology ★★★★★(2)

• HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective) from Columbia University ★★★★★(2)

• Programming Languages, Part C from University of Washington ★★★★☆(2)

• Human-Computer Interaction IV: Evaluation, Agile Methods & Beyond from Georgia Institute of Technology ★★★★★(2)

• Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud from University of Illinois at Urbana-Champaign ★★★☆☆(2)

• Advanced Data Structures in Java from University of California, San Diego ★★★★☆(2)

• Software Engineering: Introduction from The University of British Columbia ★★★☆☆(2)

• Knowledge-Based AI: Cognitive Systems from Georgia Institute of Technology ★★★☆☆(2)

• Creative Audio Programming on the Raspberry Pi from University of New South Wales ★★★★★(2)

• 6.S094: Deep Learning for Self-Driving Cars from Massachusetts Institute of Technology ★★★★☆(2)

• Fundamentals of Machine Learning in Finance from New York University (NYU) ★★☆☆☆(2)

• Reinforcement Learning in Finance from New York University (NYU) ★☆☆☆☆(2)

• The Critical Role of IT Support Staff in Healthcare from Johns Hopkins University ★★★★☆(2)

• Introduction to Algorithms (SMA 5503) from Massachusetts Institute of Technology ★★★★★(2)

• Stanford CS547 - Human-Computer Interaction Seminar Series from Stanford University ★★★★★(2)

• Stanford Seminar - How to Compute with Schrödinger's Cat: An Introduction to Quantum Computing from Stanford University ★★★★☆(2)

• Deep Learning Lecture Series from University College London ★★★★★(2)

• Unsupervised Biomedical Image Segmentation using Hyperbolic Representations | Jeffrey Gu from Stanford University ★★★★☆(2)

• Computability, Complexity & Algorithms from Georgia Institute of Technology ★★★★★(1)

• Computer Science: Programming with a Purpose from Princeton University ★★★★★(1)

• High Performance Computer Architecture from Georgia Institute of Technology ★★★★★(1)

• IoT Devices from University of Illinois at Urbana-Champaign ★★★★★(1)

• Deep Learning for Natural Language Processing from University of Oxford ★★★★★(1)

• Natural Language Processing: Foundations from National University of Singapore ★★★★★(1)

• Health Informatics: A Current and Historical Perspective from Georgia Institute of Technology ★★★★★(1)

• Introduction to Self-Driving Cars from University of Toronto ★★★★★(1)

• HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Clinical Perspective) from Columbia University ★★★★☆(1)

• Compilers from Stanford University ★★★★★(1)

• Human-Computer Interaction III: Ethics, Needfinding & Prototyping from Georgia Institute of Technology ★★★★★(1)

• Python Project: Software Engineering and Image Manipulation from University of Michigan ★★★☆☆(1)

• Graph Search, Shortest Paths, and Data Structures from Stanford University ★☆☆☆☆(1)

• Advanced Algorithms and Complexity from University of California, San Diego ★★★☆☆(1)

• Foundations of Healthcare Systems Engineering from Johns Hopkins University ★★★★★(1)

• Blockchains, Tokens, and The Decentralized Future from University of Illinois at Urbana-Champaign ★★★☆☆(1)

• Data Structures and Algorithm Design Part II | 数据结构与算法设计(下) from Tsinghua University ★★★★★(1)

• The Quantum Internet and Quantum Computers: How Will They Change the World? from Delft University of Technology ★★☆☆☆(1)

• Health Information Technology Fundamentals from Johns Hopkins University ★★☆☆☆(1)

• Software Construction: Object-Oriented Design from The University of British Columbia ★★☆☆☆(1)

• Computational Thinking for Modeling and Simulation from Massachusetts Institute of Technology ★★☆☆☆(1)

• Software Construction: Data Abstraction from The University of British Columbia ★★★☆☆(1)

• Overview of Advanced Methods of Reinforcement Learning in Finance from New York University (NYU) ★☆☆☆☆(1)

• Nature, in Code: Biology in JavaScript from École Polytechnique Fédérale de Lausanne ★★★☆☆(1)

• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★☆(1)

• Artificial Intelligence for Breast Cancer Detection from Johns Hopkins University ★★★★☆(1)

• The AI Awakening: Implications for the Economy and Society from Stanford University ★★★★☆(1)

• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(1)

• Introduction to Computer Science and Programming (Fall 2008) from Massachusetts Institute of Technology ★★★★★(1)

• Deep Learning for Computer Vision from University of Michigan ★★★★★(1)

• Design and Analysis of Algorithms from Massachusetts Institute of Technology ★★★★★(1)

• Beyond Cryptocurrency: Blockchain for the Real World from Stanford University ★★★★☆(1)

• Stanford Seminar - How Not to Generate Random Numbers from Stanford University ★★★★★(1)

• Stanford Seminar - Building the Smartest and Open Virtual Assistant to Protect Privacy - Monica Lam from Stanford University ★★★★★(1)

• Stanford Webinar - How ChatGPT and Generative AI Will Shape the Future of Work from Stanford University ★☆☆☆☆(1)

• EI Seminar - Grey Yang - Tuning GPT-3 on a Single GPU via Zero-Shot Hyperparameter Transfer from Massachusetts Institute of Technology ★★★★★(1)

• Stanford Webinar: IOT - From Smart Sensors to Smart Networks from Stanford University ★★★★★(1)

• Stanford Seminar - Rethinking the AI-UX Boundary for Designing Human-AI Experiences from Stanford University ★★★★★(1)

• Generalization and Personalization in Federated Learning | Karan Singhal from Stanford University ★★★★★(1)

• MedAI: Graph-based modeling in computational pathology | Siyi Tang from Stanford University ★★★★★(1)

• EI Seminar - Martin Riedmiller - Learning Controllers - From Engineering to AGI from Massachusetts Institute of Technology ★★★★☆(1)

• Stanford Seminar - Toward Scalable Autonomy - Aleksandra Faust from Stanford University ★★★★☆(1)

• Regulatory evaluation of image processing software devices from Yale University ★★★★★(1)

• Stanford Seminar: Self-Driving Cars for Everyone from Stanford University ★★★★★(1)

• MIT EI Seminar - Russ Tedrake - Feedback control from pixels from Massachusetts Institute of Technology ★★★☆☆(1)

• MIT 6.S191 (2021): Deep Generative Modeling from Alexander Amini ★★★★★(1)

• Wireless Above 100GHz from New York University (NYU) ★★★☆☆(1)

• Stanford Seminar: Google's Multilingual Neural Machine Translation System from Stanford University ★★★★★(1)

• ChatGPT Teach-Out from University of Michigan

• Computational Thinking for Problem Solving from University of Pennsylvania

• Algorithms: Design and Analysis, Part 1 from Stanford University

• Generative AI Teach-Out from University of Michigan

• Computer Science: Algorithms, Theory, and Machines from Princeton University

• Cyber-Physical Systems Design & Analysis from Georgia Institute of Technology

• Decision Making and Reinforcement Learning from Columbia University

• HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Social/Peer Perspective) from Columbia University

• Introduction to Quantum Computing from The University of British Columbia

• Artificial Intelligence from Georgia Institute of Technology

• Hacker Tools from Massachusetts Institute of Technology

• Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming from Stanford University

• Internet of Things: Multimedia Technologies from University of California, San Diego

• Programming & Data Structures from Columbia University

• Human-Computer Interaction from Georgia Institute of Technology

• Software Defined Networking from The University of Chicago

• Number Theory and Cryptography from University of California, San Diego

• AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning from Delft University of Technology

• Internet of Things: Sensing and Actuation From Devices from University of California, San Diego

• Machine Learning for Semiconductor Quantum Devices from Delft University of Technology

• Getting started with TensorFlow 2 from Imperial College London

• High Performance Computing from Georgia Institute of Technology

• The Data Science of Health Informatics from Johns Hopkins University

• Fundamentals of Machine Learning for Healthcare from Stanford University

• IoT Networking from University of Illinois at Urbana-Champaign

• Machine Learning and AI with Python from Harvard University

• Introduction to Blockchain Technology and Applications from University College London

• Algorithms: Design and Analysis, Part 2 from Stanford University

• Development and Applications of Germanium Quantum Technologies from Delft University of Technology

• Ethics in AI Design from Delft University of Technology

• Introduction to Clinical Data from Stanford University

• Computer Applications of Artificial Intelligence and e-Construction from Purdue University

• Health Informatics: Data and Interoperability Standards from Georgia Institute of Technology

• Generative AI: Fundamentals, Applications, and Challenges from University of Michigan

• Artificial Intelligence Essentials from University of Pennsylvania

• AI skills for Engineers: Supervised Machine Learning from Delft University of Technology

• Evaluations of AI Applications in Healthcare from Stanford University

• AI Applications in People Management from University of Pennsylvania

• AI in Healthcare Capstone from Stanford University

• Machine Learning and Human Learning from University of Illinois at Urbana-Champaign

• Camera and Imaging from Columbia University

• Deep Learning Methods for Healthcare from University of Illinois at Urbana-Champaign

• The Social and Technical Context of Health Informatics from Johns Hopkins University

• Introduction to Computational Science and Engineering from Massachusetts Institute of Technology

• Machine Learning: Concepts and Applications from The University of Chicago

• Generative AI: Impact on Business and Society from University of Michigan

• Input and Interaction from University of California, San Diego

• IoT Communications from University of Illinois at Urbana-Champaign

• Assessment Design with AI from Georgia Institute of Technology

• Introduction to Machine Learning in Sports Analytics from University of Michigan

• AI Strategy and Governance from University of Pennsylvania

• AI for Teacher Assistance from Georgia Institute of Technology

• Search Engines for Web and Enterprise Data from The Hong Kong University of Science and Technology

• Introduction to Scientific Machine Learning from Purdue University

• Generative AI: Labor and the Future of Work from University of Michigan

• Complex Problem Structuring: a Socio-Technical Perspective from Delft University of Technology

• Advanced Deep Learning Methods for Healthcare from University of Illinois at Urbana-Champaign

• Shortest Paths Revisited, NP-Complete Problems and What To Do About Them from Stanford University

• Introduction to Deep Learning from Purdue University

• Statistical Machine Learning from Carnegie Mellon University

• Creative Coding for Designers Using Python from University of Michigan

• Network Function Virtualization from Georgia Institute of Technology

• Blockchain Scalability and its Foundations in Distributed Systems from The University of Sydney

• AI Applications in Marketing and Finance from University of Pennsylvania

• Moving to the Cloud from University of Melbourne

• Image Processing and Analysis for Life Scientists from École Polytechnique Fédérale de Lausanne

• Compilers: Theory and Practice from Georgia Institute of Technology

• Chatbots for Instruction from Georgia Institute of Technology

• Probabilistic Graphical Models 3: Learning from Stanford University

• Accessible Gamification from Georgia Institute of Technology

• Applied Quantum Computing I: Fundamentals from Purdue University

• Android Graphics with OpenGL ES from Imperial College London

• Machine Learning from Georgia Institute of Technology

• AI Materials from Korea Advanced Institute of Science and Technology

• Industrial Internet of Things (IIoT) from University of Michigan

• Building Autonomous AI from University of Washington

• Unsupervised Learning, Recommenders, Reinforcement Learning from DeepLearning.AI

• Health Informatics: The Cutting Edge from Georgia Institute of Technology

• Machine Learning for Accounting with Python from University of Illinois at Urbana-Champaign

• Data Structures Fundamentals from University of California, San Diego

• IoT Systems and Industrial Applications with Design Thinking from École Polytechnique Fédérale de Lausanne

• Python Fundamentals for Designers from University of Michigan

• Advanced Learning Algorithms from DeepLearning.AI

• Customising your models with TensorFlow 2 from Imperial College London

• Machine Teaching for Autonomous AI from University of Washington

• Applications of Machine Learning in Plant Science from Cornell University

• Machine Learning Use Cases in Finance from Université de Montréal

• AI Fundamentals for Non-Data Scientists from University of Pennsylvania

• Introduction to Graduate Algorithms from Georgia Institute of Technology

• Introduction to Quantum Computing for Everyone from The University of Chicago

• Visual Perception for Self-Driving Cars from University of Toronto

• Computer Vision for Embedded Systems from Purdue University

• Visual Perception from Columbia University

• Computer Graphics from University of California, San Diego

• Deep Learning Essentials from Université de Montréal

• Computing for Cancer Informatics from Johns Hopkins University

• The Outcomes and Interventions of Health Informatics from Johns Hopkins University

• Internet of Things V2: Setting up and Using Cloud Services from University of California, San Diego

• Introduction to Quantum Computing for Everyone 2 from The University of Chicago

• Solving Algorithms for Discrete Optimization from University of Melbourne

• Communications and High-Speed Signals with Raspberry Pi from Johns Hopkins University

• 3D Reconstruction - Multiple Viewpoints from Columbia University

• Data Structures for Designers Using Python from University of Michigan

• Designing Hardware for Raspberry Pi Projects from Johns Hopkins University

• IoT Cloud from University of Illinois at Urbana-Champaign

• Machine Learning Essentials from University of Pennsylvania

• Fundamentals of Quantum Information from Delft University of Technology

• Beginning Custom Projects with Raspberry Pi from Johns Hopkins University

• Motion Planning for Self-Driving Cars from University of Toronto

• Fundamentals of TinyML from Harvard University

• AI in Healthcare. Hype or Help? from KU Leuven University

• Introduction to Digital Health Entrepreneurship from Johns Hopkins University

• Graph Algorithms from University of California, San Diego

• Leadership and Management for PM Practitioners in IT from University of Washington

• MLOps for Scaling TinyML from Harvard University

• Applied Quantum Computing III: Algorithm and Software from Purdue University

• Self-Driving Cars with Duckietown from ETH Zurich

• Deploying TinyML from Harvard University

• 3D Reconstruction - Single Viewpoint from Columbia University

• State Estimation and Localization for Self-Driving Cars from University of Toronto

• Basic Modeling for Discrete Optimization from University of Melbourne

• Introduction to Quantum Information from Korea Advanced Institute of Science and Technology

• Data Augmented Technology Assisted Medical Decision Making from University of Michigan

• Data Analytics Foundations for Accountancy II from University of Illinois at Urbana-Champaign

• Mathematics for Computer Science from Massachusetts Institute of Technology

• Introduction à l'éthique de l’IA from Université de Montréal

• Information Extraction from Free Text Data in Health from University of Michigan

• Supervised Machine Learning: Regression and Classification from DeepLearning.AI

• Internet of Things V2: DragonBoard™ bring up and community ecosystem from University of California, San Diego

• Quantum Detectors and Sensors from Purdue University

• Applied Quantum Computing II: Hardware from Purdue University

• Generative AI Essentials: Overview and Impact from University of Michigan

• Data Structures and Algorithm Design Part I | 数据结构与算法设计(上) from Tsinghua University

• Designing Autonomous AI from University of Washington

• Applications of TinyML from Harvard University

• Quantum Computer Systems Design I: Intro to Quantum Computation and Programming from The University of Chicago

• Logistic Regression and Prediction for Health Data from University of Michigan

• Design and Implementation of Digital Health Interventions from Imperial College London

• Introduction to Concurrent Programming with GPUs from Johns Hopkins University

• Using Sensors With Your Raspberry Pi from Johns Hopkins University

• Advanced Modeling for Discrete Optimization from University of Melbourne

• Boltzmann Law: Physics to Computing from Purdue University

• Operations and Patient Safety for Healthcare IT Staff from Johns Hopkins University

• Probabilistic Deep Learning with TensorFlow 2 from Imperial College London

• Internet of Things Capstone: Build a Mobile Surveillance System from University of California, San Diego

• Modern Distributed Systems from Delft University of Technology

• Culminating Project in Health Informatics from Johns Hopkins University

• 计算机辅助翻译原理与实践 Principles and Practice of Computer-Aided Translation from Peking University

• Introduction to CSS3 from Johns Hopkins University

• Techniques d’intelligence artificielle : des fondements aux applications from Université de Montréal

• Portfolio Management, Governance, & the PMO from University of Washington

• iLabX – The Internet Masterclass from Technische Universität München (Technical University of Munich)

• Quantum Computer Systems Design II: Principles of Quantum Architecture from The University of Chicago

• Quantum Communication and the Quantum Network Explorer from Delft University of Technology

• Supercomputing from Partnership for Advanced Computing in Europe

• Explore Trading and Lending in Decentralized Finance from University of Toronto

• Features and Boundaries from Columbia University

• Introduction à l'apprentissage profond from Université de Montréal

• Machine Learning Algorithms with R in Business Analytics from University of Illinois at Urbana-Champaign

• Quantum Computer Systems Design III: Working with Noisy Systems from The University of Chicago

• Emerging Technology, Disruption, and AI from University of Illinois at Urbana-Champaign

• Internet of Things Capstone V2: Build a Mobile Surveillance System from University of California, San Diego

• Bias and Discrimination in AI from Université de Montréal

• Introduction to Computer Science and Programming (Spring 2011) from Massachusetts Institute of Technology

• Ordered Data Structures from University of Illinois at Urbana-Champaign

• The Society of Mind from Massachusetts Institute of Technology

• GT - Refresher - Advanced OS from Georgia Institute of Technology

• Data Structures and Algorithms (I) from Tsinghua University

• Llama for Python Programmers from University of Michigan

• 操作系统原理（Operating Systems） from Peking University

• String Processing and Pattern Matching Algorithms from University of California, San Diego

• 算法设计与分析 Design and Analysis of Algorithms from Peking University

• Biais et discrimination en IA from Université de Montréal

• Understanding the World Through Data - from Massachusetts Institute of Technology

• Leveraging Generative AI for Social Impact Organizations from University of Michigan

• Data Structures and Algorithms (III) from Tsinghua University

• Advanced Data Structures from Massachusetts Institute of Technology

• NP-Complete Problems from University of California, San Diego

• Data Structures and Algorithms (IV) from Tsinghua University

• Unordered Data Structures from University of Illinois at Urbana-Champaign

• Blockchain Technology and the Future of FinTech from University of Toronto

• Data Structures and Algorithms (II) from Tsinghua University

• L'essentiel de l'apprentissage profond from Université de Montréal

• AI in Practice: Preparing for AI from Delft University of Technology

• Deploying Machine Learning Models from University of California, San Diego

• Big Data Machine Learning | 大数据机器学习 from Tsinghua University

• MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018 from Massachusetts Institute of Technology

• Les coulisses des systèmes de recommandation from Université de Montréal

• Dive Into the World of Blockchain: Principles, Mechanics, and Tokens from University of Toronto

• LAFF – On Programming for Correctness from The University of Texas at Austin

• Vision artificielle et exploitation intelligente des ressources naturelles from Université de Montréal

• LAFF-On Programming for High Performance from The University of Texas at Austin

• 计算几何 | Computational Geometry from Tsinghua University

• Optimization: principles and algorithms - Network and discrete optimization from École Polytechnique Fédérale de Lausanne

• Recommender Systems: Behind the Screen from Université de Montréal

• Decoding AI: A Deep Dive into AI Models and Predictions from University of Michigan

• 离散优化算法篇 Solving Algorithms for Discrete Optimization from The Chinese University of Hong Kong

• Deep Learning in Life Sciences - Spring 2021 from Massachusetts Institute of Technology

• Optimization: principles and algorithms - Unconstrained nonlinear optimization from École Polytechnique Fédérale de Lausanne

• 计算机组成 Computer Organization from Peking University

• Navigating Decentralized Derivatives and Governance in Blockchain from University of Toronto

• AI in Practice: Applying AI from Delft University of Technology

• Introduction to Computational Thinking from Massachusetts Institute of Technology

• 数据结构基础 from Peking University

• Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018 from Stanford University

• 算法基础 from Peking University

• Stanford Webinar - How [You] Can Use ChatGPT to Increase Your Creative Output from Stanford University

• 高级数据结构与算法 from Peking University

• MIT 6.01SC Introduction to EECS I from Massachusetts Institute of Technology

• The Battlecode Programming Competition from Massachusetts Institute of Technology

• Geometric Folding Algorithms: Linkages, Origami, Polyhedra from Massachusetts Institute of Technology

• Programming for the Puzzled (January IAP 2018) from Massachusetts Institute of Technology

• Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 from Stanford University

• Stanford CS105 - Introduction to Computers Full Course from Stanford University

• Stanford Seminar - Software-Defined Networking at the Crossroads from Stanford University

• CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3 from Stanford University

• Algorithmic Lower Bounds: Fun with Hardness Proofs from Massachusetts Institute of Technology

• 操作系统与虚拟化安全 from Peking University

• MIT 18.404J Theory of Computation, Fall 2020 from Massachusetts Institute of Technology

• Stanford Seminar - Deep Learning for Medical Diagnoses from Stanford University

• 离散优化建模基础篇 Basic Modeling for Discrete Optimization from The Chinese University of Hong Kong

• Multicore Programming Primer from Massachusetts Institute of Technology

• 离散优化建模高阶篇 Advanced Modeling for Discrete Optimization from The Chinese University of Hong Kong

• Stanford Seminar - The FATE of AI Ethics, Anna Bethke from Stanford University

• Machine Learning Course - CS 156 from California Institute of Technology

• Artificial Intelligence Planning from University of Edinburgh

• Stanford Seminar - Representation Learning for Autonomous Robots, Anima Anandkumar from Stanford University

• Stanford Seminar - Deep Learning in Speech Recognition from Stanford University

• 算法设计与分析(高级) | Advanced Design and Analysis of Algorithms from Peking University

• Stanford CS234: Reinforcement Learning | Winter 2019 from Stanford University

• Stanford Seminar - Using Data for Increased Realism with Haptic Modeling and Devices from Stanford University

• Stanford Seminar - Enabling NLP, Machine Learning, & Few-Shot Learning using Associative Processing from Stanford University

• Stanford Seminar - Designing Assistive Technologies for Agency from Stanford University

• Stanford Seminar - Creating Interfaces with Rich Physical Properties Through Digital Fabricationity from Stanford University

• Stanford Seminar - Natural Language Processing for Production-Level Conversational Interfaces from Stanford University

• Stanford Seminar - Software-centric Visible Light Communication for the Internet of Things from Stanford University

• MIT EI Seminar - Phillip Isola - Emergent Intelligence: getting more out of agents than you bake in from Massachusetts Institute of Technology

• MIT 6.S191: Reinforcement Learning from Alexander Amini

• Stanford Seminar - Neural Networks on Chip Design from the User Perspective from Stanford University

• Stanford Seminar - Training Classifiers with Natural Language Explanations from Stanford University

• Stanford Seminar - Bugs in Crypto Implementations from Stanford University

• Stanford Seminar - Can the brain do back-propagation? from Stanford University

• Federated Hyperparameters Tuning: Challenges, Baselines & Connections | Mikhail Khodak from Stanford University

• Frontiers of Medical AI - Therapeutics and Workflows | Andre Esteva from Stanford University

• Алгоритмы, часть I from Princeton University

• EI Seminar - Rob Fergus - Data Augmentation for Image-Based Reinforcement Learning from Massachusetts Institute of Technology

• Stanford Seminar - Citadel of One: Individuality and the rise of the machines, Suzanne Sadedin from Stanford University

• Weakly-supervised, large-scale computational pathology for diagnosis & prognosis | Max Lu from Stanford University

• Rethinking Architecture Design for Data Heterogeneity in FL | Liangqiong Qu from Stanford University

• EI Seminar - Recent papers in Embodied Intelligence from Massachusetts Institute of Technology

• EI Seminar - Yejin Choi - Intuitive Reasoning as (Un)supervised Neural Generation from Massachusetts Institute of Technology

• EI Seminar - Michael Carbin - The Lottery Ticket Hypothesis from Massachusetts Institute of Technology

• MIT EI Seminar - Lerrel Pinto - Diverse data and efficient algorithms for robot learning from Massachusetts Institute of Technology

• MIT 6.S191 (2020): Neurosymbolic AI from Alexander Amini

• MIT 6.S191: Towards AI for 3D Content Creation from Alexander Amini

• MIT 6.S191: AI in Healthcare from Alexander Amini

• MIT Introduction to Deep Learning | 6.S191 from Alexander Amini

• MIT 6.S191: Deep Generative Modeling from Alexander Amini

• MIT 6.S191 (2018): Convolutional Neural Networks from Alexander Amini

• MIT 6.S191 (2018): Beyond Deep Learning: Learning+Reasoning from Alexander Amini

• MIT 6.S191 (2019): Visualization for Machine Learning (Google Brain) from Alexander Amini

• Deep Maths - machine learning and mathematics from University of Oxford

• The Creativity Code - Marcus du Sautoy from University of Oxford

• CS154 Stanford - Introduction to the Theory of Computing from Stanford University

• Stanford CS224W - Machine Learning with graphs- Fall 2019 from Stanford University

• Stanford Seminar - Scalable Intelligent Systems Build and Deploy by 2025 from Stanford University

• Stanford Seminar - Smart Physical Systems from the Standpoint of an AI Company from Stanford University

• Deep Learning Methods for Electrocardiograms and Echocardiograms | Weston Hughes from Stanford University

• Towards Generalist Imaging Using Multimodal Self-supervised Learning | Mars Huang from Stanford University

• Few-shot Chest X-ray Diagnosis Using Clinical & Literature Images | Angshuman Paul from Stanford University

• Ge Wang: The Artful Design of Interactive AI Systems from Stanford University

• Stanford Seminar - Federated Learning in Medicine: Breaking Down Silos to Advance Medical Research from Stanford University

• Stanford Seminar - Gender Disparities in Software Engineering from Stanford University

• EI Seminar - Luke Zettlemoyer - Large Language Models: Will they keep getting bigger? from Massachusetts Institute of Technology

• Daniel Wolpert - Computational principles underlying the learning of sensorimotor repertoires from Massachusetts Institute of Technology

• EI Seminar - Jacob Andreas - Natural Language Explanations of Deep Networks from Massachusetts Institute of Technology

• MIT 6.S191: AI Bias and Fairness from Alexander Amini

• MIT 6.S191: Recurrent Neural Networks and Transformers from Alexander Amini

• MIT 6.S191: Automatic Speech Recognition from Alexander Amini

• MIT 6.S191 (2018): Deep Learning Limitations and New Frontiers from Alexander Amini

• MIT 6.S191 (2019): Introduction to Deep Learning from Alexander Amini

• MIT 6.S191 (2018): Introduction to Deep Learning from Alexander Amini

• Improved ultrasound image formation—domain adaptation with no data from Yale University

• Stanford CS330: Deep Multi-Task and Meta Learning from Stanford University

• Stanford Seminar - Failures & Where to Find Them: Considering Safety as a Function of Structure from Stanford University

• Stanford Seminar - Deep Learning for Symbolic Mathematics - Guillaume Lample & Francois Charton from Stanford University

• Stanford Seminar - Lenia: Biology of Artificial Life, Bert Wang-Chak Chan from Stanford University

• Stanford Seminar - Leela: a Semantic Intelligent Agent from Stanford University

• Stanford Seminar - Safe passwords made easy to use from Stanford University

• Stanford Seminar - An architect's point of view on emerging technologies from Stanford University

• Stanford Seminar - The Quest for Low Storage Latency Changes Everything from Stanford University

• Stanford Seminar: The REX Neo Architecture: An energy efficient new processor architecture from Stanford University

• Stanford Seminar: Concepts and Questions as Programs from Stanford University

• Stanford Seminar - Dynamic Code Optimization and the NVIDIA Denver Processor from Stanford University

• Stanford Seminar - Applying Theory to Practice (and Practice to Theory) from Stanford University

• Stanford Seminar - Instruction Sets Should Be Free: The Case for RISC-V from Stanford University

• Denoising diffusion models for denoising diffusion MRI | Tiange Xiang from Stanford University

• Solana Larsen: Who has Power over AI? from Stanford University

• Stanford Webinar - Web3 Considered: Possible Futures for Decentralization and Digital Ownership from Stanford University

• Stanford Seminar - Rebooting the Internet from Stanford University

• EI Seminar - Ruslan Salakhutdinov - Building Embodied Autonomous Agents from Massachusetts Institute of Technology

• EI Seminar - Recent papers in Embodied Intelligence I from Massachusetts Institute of Technology

• MIT 6.S191: Convolutional Neural Networks from Alexander Amini

• MIT 6.S191 (2018): Sequence Modeling with Neural Networks from Alexander Amini

• MIT 6.S191 (2018): Computer Vision Meets Social Networks from Alexander Amini

• MIT 6.S191 (2019): Recurrent Neural Networks from Alexander Amini

• MIT 6.S191 (2019): Convolutional Neural Networks from Alexander Amini

• 6.036 Introduction to Machine Learning from Massachusetts Institute of Technology

• Stanford Seminar - Forecasting and Aligning AI, Jacob Steinhardt from Stanford University

• Stanford Seminar - Erudite: Prototype System for Computational Intelligence from Stanford University

• Stanford Seminar - Efficient and Resilient Systems in the Cognitive Era from Stanford University

• Stanford Seminar - SMILE: Synchronized, Multi-sensory Integrated Learning Environment from Stanford University

• Stanford Seminar - New Golden Age for Computer Architecture - John Hennessy from Stanford University

• Stanford Seminar: Dialog Markets from Stanford University

• Stanford Seminar: Time Traveling Hardware and Software Systems from Stanford University

• Stanford Seminar: HPC Opportunities in Deep Learning - Greg Diamos, Baidu from Stanford University

• Stanford Seminar - Instruction execution on the Mill CPU from Stanford University

• Stanford Seminar - Mostly Missless Memory in the Mill CPU from Stanford University

• Opioid Use Disorder Prediction Using AI and Existing Risk Models | Sajjad Fouladvand from Stanford University

• Real-Time Seizure Detection using EEG | Hyewon Jeong from Stanford University

• Multimodal opportunistic risk assessment for ischemic heart disease | Juan Manuel from Stanford University

• MedAI: Learning the Structure of EHR with Graph Convolutional Transformer | Edward Choi from Stanford University

• Akshay Chaudhari: Data-Efficient AI for Accelerating MRI Acquisition from Stanford University

• Chris Re: How Machine Learning is Changing Software from Stanford University

• Against Ethical Robots with Ron Chrisley from Stanford University

• Stanford Webinar - Artificial Intelligence for Business Leaders from Stanford University

• AI and Law I Mariano-Florentino Cuéllar, President of the Carnegie Endowment for International Peace from Stanford University

• Stanford Seminar - Accelerating ML Recommendation with over a Thousand RISC-V/Tensor Processors... from Stanford University

• Stanford Webinar - Design & AI: Envisioning AI-augmented Products from Stanford University

• Stanford CS229: Machine Learning | Summer 2019 | Lecture 8 - Kernel Methods & Support Vector Machine from Stanford University

• Part Two! How [You] Can Use ChatGPT to Increase Your Creative Output from Stanford University

• CS25 I Stanford Seminar - Transformers United: Neuroscience-Inspired Artificial Intelligence from Stanford University

• Getting Robust: Securing Neural Networks against Adversarial Attacks from University of Melbourne

• Can you trust a computer algorithm… how and why? from University of Melbourne

• MIT EI Seminar - Pulkit Agrawal - The Task Specification Problem from Massachusetts Institute of Technology

• MIT 6.S191 (2020): Machine Learning for Scent from Alexander Amini

• MIT 6.S191 (2021): Introduction to Deep Learning from Alexander Amini

• MIT 6.S191 (2021): Recurrent Neural Networks from Alexander Amini

• MIT 6.S191 (2021): Convolutional Neural Networks from Alexander Amini

• MIT 6.S191: Taming Dataset Bias via Domain Adaptation from Alexander Amini

• MIT 6.S191 (2019): Deep Generative Modeling from Alexander Amini

• MIT 6.S191 (2019): Biologically Inspired Neural Networks (IBM) from Alexander Amini

• MIT 6.S191 (2020): Introduction to Deep Learning from Alexander Amini

• MIT 6.S191 (2020): Recurrent Neural Networks from Alexander Amini

• MIT 6.S191 (2020): Deep Generative Modeling from Alexander Amini

• Modeling prior information to guide image reconstruction in radiation therapy from Yale University

• 程序开发项目实践 from Peking University

• 区块链和加密数字货币 from Tsinghua University

• The Evolution of Public Key Cryptography from New York University (NYU)

• Ideas for a Complex World - Anna Seigal from University of Oxford

• GGP Course Videos from Stanford University

• Stanford Seminar - Persistent and Unforgeable Watermarks for DeepNeural Networks from Stanford University

• Stanford Seminar - MIPS Open, Wave Computing from Stanford University

• Stanford Seminar - Computational memory: A stepping-stone to non-von Neumann computing? from Stanford University

• Stanford Seminar - Distributed Perception and Learning Between Robots and the Cloud from Stanford University

• Stanford Seminar: Computational Ecosystems from Stanford University

• Explaining Model Decisions and Fixing Them through Human Feedback | Ramprasaath Selvaraju from Stanford University

• Fairness in representation learning | Natalie Dullerud from Stanford University

• Actionable Machine Learning for Tackling Distribution Shift | Huaxiu Yao from Stanford University

• Generative models with domain knowledge for weakly supervised clustering | Laura Manduchi from Stanford University

• Domain Adaptation with Invariant Representation Learning | Petar Stojanov from Stanford University

• Efficiently Modeling Long Sequences with Structured State Spaces | Albert Gu from Stanford University

• Optimizing for Interpretability in Deep Neural Networks | Mike Wu from Stanford University

• GANs in Medical Image Synthesis, Translation, and Augmentation | Jason Jeong from Stanford University

• Adversarial debiasing with partial learning - medical image studies | Ramon Correa from Stanford University

• Assignment Control Plots for Causal Inference Study Design | Rocky Aikens from Stanford University

• Observational Supervision for Medical Image Classification | Khaled Saab from Stanford University

• Self-training/Weak Supervision + Untrained Neural Nets for MR Recon | Beliz Gunel from Stanford University

• Mandoline - Model Evaluation under Distribution Shift | Mayee Chen from Stanford University

• Style transfer augmentations for computational pathology | Rikiya Yamashita from Stanford University

• MedAI: Self-supervision & Contrastive Frameworks: a vision-based review | Nandita Bhaskhar from Stanford University

• Segmentation and Quantification of Breast Arterial Calcifications | Xiaoyuan Guo from Stanford University

• HAI Weekly Seminar with Jeff Ding: The Rise and Fall of Great Technologies and Powers from Stanford University

• Agrim Gupta: Towards Understanding and Building Embodied Intelligence from Stanford University

• Jiajun Wu: Learning to See the Physical World from Stanford University

• Stanford Seminar - Human-AI Interaction Under Societal Disagreement from Stanford University

• Stanford Seminar - Why would we want a multi-agent system unstable from Stanford University

• Stanford Lecture: Don Knuth - Twintrees, Baxter Permutations, and Floorplans (2022) from Stanford University

• Stanford Webinar - Cryptocurrencies and Blockchains: The Science Behind the Technology, Dan Boneh from Stanford University

• CS25 I Stanford Seminar 2022 - Self Attention and Non-parametric transformers (NPTs) from Stanford University

• Stanford Seminar - Lessons from evaluating and debugging healthcare AI in deployment from Stanford University

• Stanford Seminar - Safety (and Liveness!) of Robot Behaviors from Stanford University

• Stanford Seminar - Is Google Search Is Dying? from Stanford University

• Stanford Seminar - Interactive Imitation Learning: Planning Alongside Humans from Stanford University

• Stanford Seminar - 4004 Microprocessors from Stanford University

• Stanford Webinar with Dan Boneh - Hacking AI: Security & Privacy of Machine Learning Models from Stanford University

• Stanford Seminar - Computer-designed organisms - Josh Bongard from Stanford University

• Stanford Seminar - Edge Computing in Autonomous Vehicles (panel discussion) from Stanford University

• Stanford Seminar - Nanosecond-level Clock Synchronization in a Data Center from Stanford University

• Stanford Lecture: Don Knuth—"Dancing Links" (2018) from Stanford University

• Stanford Seminar - What's Next for Blockchain in China? from Stanford University

• Stanford Seminar - Scoring (automatically) news articles to fight misinformation from Stanford University

• Stanford Seminar - The Evolution of Public Key Cryptography from Stanford University

• Stanford Lecture: Don Knuth—"A Conjecture That Had To Be True" (2017) from Stanford University

• Stanford Seminar - NVIDIA GPU Computing: A Journey from PC Gaming to Deep Learning from Stanford University

• Stanford Seminar - Unethical Algorithms of Massive Scale from Stanford University

• Stanford Seminar - Creating Human-Computer Partnerships from Stanford University

• Stanford Webinar - Cybersecurity in Modern Era: Zero Knowledge Proofs Explained from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 4 - Maximum Likelihood Learning from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 6 - VAEs from Stanford University

• Stanford Seminar - When Design = Planning from Stanford University

• Stanford Webinar - AI in the Workplace: Rethinking Skill Development from Stanford University

• Information, Calcul, Communication: Introduction à la pensée informatique from École Polytechnique Fédérale de Lausanne

• Race, Technology, and Algorithmic Bias | Vision & Justice || Radcliffe Institute from Harvard University

• Finding Fairness | Cynthia Dwork || Radcliffe Institute from Harvard University

• CS50 2012 / Week 3, continued from Harvard University

• MIT 6.S191 (2020): Neural Rendering from Alexander Amini

• MIT 6.S191 (2020): Generalizable Autonomy for Robot Manipulation from Alexander Amini

• MIT 6.S191 (2020): Deep Learning New Frontiers from Alexander Amini

• MIT 6.S191 (2021): Reinforcement Learning from Alexander Amini

• MIT 6.S191 (2021): Deep Learning New Frontiers from Alexander Amini

• MIT 6.S191: Evidential Deep Learning and Uncertainty from Alexander Amini

• MIT 6.S191: Deep CPCFG for Information Extraction from Alexander Amini

• GTC 2018: Learning Steering for Parallel Autonomy. Alexander Amini from Alexander Amini

• MIT 6.S191 (2019): Deep Reinforcement Learning from Alexander Amini

• MIT 6.S191 (2019): Deep Learning Limitations and New Frontiers from Alexander Amini

• MIT 6.S191 (2019): Image Domain Transfer (NVIDIA) from Alexander Amini

• MIT 6.S191 (2020): Convolutional Neural Networks from Alexander Amini

• MIT 6.S191 (2020): Reinforcement Learning from Alexander Amini

• Introduction to CS and Programming using Python from Massachusetts Institute of Technology

• Information Technology as an Integrating Force in Manufacturing from Massachusetts Institute of Technology

• Model-based deep learning: beyond unrolling from Yale University

• AI in Molecular Imaging from Yale University

• The Era of Artificial Intelligence from New York University (NYU)

• TensorFlow 2 시작하기 from Imperial College London

• MedAI: Training medical image segmentation models with less labeled data | Sarah Hooper from Stanford University

• Multimodal medical research of vision and language | Jean-Benoit Delbrouck from Stanford University

• David Robinson: Voices in the Code: A Story About People, Their Values, and the Algorithm They Made from Stanford University

• Kathleen Creel: Picking on the Same Person: Does Algorithmic Monoculture Homogenize Outcomes? from Stanford University

• Carlos Guestrin: How Can You Trust Machine Learning? from Stanford University

• Karen Liu: The New Role of Physics Simulation in AI from Stanford University

• Leonidas Guibas: Joint Learning Over Visual and Geometric Data from Stanford University

• Kathleen Creel: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems from Stanford University

• Stanford Seminar - Highly optimized quantum circuits synthesized via data-flow engines from Stanford University

• Stanford Seminar - Optimizing the Internet from Stanford University

• Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data from Stanford University

• CS25 I Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning from Stanford University

• CS25 I Stanford Seminar 2022 - DeepMind's Perceiver and Perceiver IO: new data family architecture from Stanford University

• CS25 I Stanford Seminar 2022 - Mixture of Experts (MoE) paradigm and the Switch Transformer from Stanford University

• CS25 I Stanford Seminar 2022 - Decision Transformer: Reinforcement Learning via Sequence Modeling from Stanford University

• CS25 I Stanford Seminar 2022 - Transformers in Vision: Tackling problems in Computer Vision from Stanford University

• Stanford Seminar - Computing with Physical Systems from Stanford University

• Stanford Talk: Inequality in Healthcare, AI & Data Science to Reduce Inequality - Improve Healthcare from Stanford University

• Stanford Seminar - How can you trust machine learning? Carlos Guestrin from Stanford University

• Stanford Seminar - Dataflow for convergence of AI and HPC - GroqChip! from Stanford University

• Stanford Seminar - Recent progress in verifying neural networks, Zico Kolter from Stanford University

• Stanford Seminar - Emerging risks and opportunities from large language models, Tatsu Hashimoto from Stanford University

• The Race for Technological Supremacy: Emerging Technologies and Global Implications from Stanford University

• Stanford Seminar - Universal Intelligent Systems by 2030 - Carl Hewitt and John Perry from Stanford University

• Stanford Webinar - The Future of Blockchain and Cryptocurrencies: Dan Boneh from Stanford University

• Stanford Webinar - Create a Better User Experience through AI, Michael Bernstein from Stanford University

• Improving Natural Language Understanding through Adversarial Testing from Stanford University

• Stanford Seminar - The Soul of a New Machine: Rethinking the Computer from Stanford University

• Stanford Seminar - fastai: A Layered API for Deep Learning from Stanford University

• Stanford Seminar - Current Status of tinyML and the Enormous Opportunities Ahead (panel discussion) from Stanford University

• Stanford Seminar - Finding the Great Problems from Stanford University

• Stanford Seminar - New Chip Technologies for AI and for Asia: the Edge is Where the Action is! from Stanford University

• Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 - Adversarial Attacks / GANs from Stanford University

• Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 - Full-Cycle Deep Learning Projects from Stanford University

• Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 - Deep Learning Intuition from Stanford University

• Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng from Stanford University

• Stanford Seminar - Extending the theory of ML for human-centric applications from Stanford University

• Stanford Seminar - How is AI Evolving? A Look at the Startup Landscape in the U.S. and Asia from Stanford University

• Stanford Seminar - Autonomous Driving: AI's Biggest Endeavor, James Peng of Pony.ai from Stanford University

• Stanford Seminar - Virtual Human Agent for Smart City from Stanford University

• Stanford Seminar - The Case for Learned Index Structures from Stanford University

• Stanford Seminar- Taraxa.io: A Globalized Blockchain Protocol Startup from Stanford University

• Stanford Seminar - Artificial Intelligence: Current and Future Paradigms and Implications from Stanford University

• Stanford Seminar - Exploring the implications of machine learning for cognitive disabilities from Stanford University

• Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby from Stanford University

• Stanford Seminar - Tiny functions for codecs, compilation, and (maybe) soon everything from Stanford University

• Stanford Seminar - Entrepreneurial Lessons from Self-Driving Cars from Stanford University

• Stanford Seminar - Petascale Deep Learning on a Single Chip from Stanford University

• Stanford Seminar - Time Well Spent, Tristan Harris from Stanford University

• Stanford Seminar - Computing with High-Dimensional Vectors from Stanford University

• Stanford Seminar - Multiscale Dataflow Computing: Competitive Advantage at the Exascale Frontier from Stanford University

• Stanford Seminar: Autonomous Driving, are we there yet? - Technology, Business, Legal Considerations from Stanford University

• Stanford Seminar: Beyond Floating Point: Next Generation Computer Arithmetic from Stanford University

• Stanford Seminar: Deep Learning in the Age of Zen, Vega, and Beyond from Stanford University

• Stanford Lecture - Don Knuth: The Analysis of Algorithms (2015, recreating 1969) from Stanford University

• Stanford Seminar: Designing the iPhone's Magic Flute - Ge Wang from Stanford University

• Stanford Seminar: Challenges in Secure Messaging from Stanford University

• Stanford Seminar: The Time-Less Datacenter from Stanford University

• Stanford Seminar - Runway: A New Tool for Distributed Systems Design from Stanford University

• Bradford Parkinson "GPS for Humanity" - Stanford Engineering Hero Lecture from Stanford University

• Stanford Seminar - Leveraging Social Theories to Enhance Human-AI Interaction from Stanford University

• Stanford CS109 I Fairness I 2022 I Lecture 26 from Stanford University

• Stanford Lecture: Don Knuth - Twintrees, Baxter Permutations, and Floorplans (2022) from Stanford University

• Stanford CS25: V3 I Beyond LLMs: Agents, Emergent Abilities, Intermediate-Guided Reasoning, BabyLM from Stanford University

• Stanford Webinar - Democratizing Model Discovery with Neural Networks from Stanford University

• Stanford CS25: V3 I How I Learned to Stop Worrying and Love the Transformer from Stanford University

• Stanford Lecture - Dancing Cells, Dr. Don Knuth I 2023 from Stanford University

• Stanford Seminar - The State of Design Knowledge in Human-AI Interaction from Stanford University

• Stanford Seminar - Towards Safe and Efficient Learning in the Physical World from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 18 - Video Compression from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 1 - Course Intro, Lossless Data Compression Basics from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 2 - Prefix Free Codes from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 5 - Asymptotic Equipartition Property from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 9 - Context-based AC & LLM Compression from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 4 - Huffman Codes from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 3 - Kraft Inequality, Entropy, Introduction to SCL from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 8 - Beyond IID distributions: Conditional entropy from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 10 - LZ and Universal Compression from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 6 - Arithmetic Coding from Stanford University

• Stanford EE274: Data Compression I 2023 I Lecture 7 - ANS from Stanford University

• Stanford CS25: V4 I Overview of Transformers from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 3 - Autoregressive Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 17 - Discrete Latent Variable Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 13 - Score Based Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 5 - VAEs from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 16 - Score Based Diffusion Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 18 - Diffusion Models for Discrete Data from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 2 - Background from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 12 - Energy Based Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 9 - Normalizing Flows from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 14 - Energy Based Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 15 - Evaluation of Generative Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 11 - Energy Based Models from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 7 - Normalizing Flows from Stanford University

• Stanford CS236: Deep Generative Models I 2023 I Lecture 1 - Introduction from Stanford University

• Stanford CS25: V4 I Jason Wei & Hyung Won Chung of OpenAI from Stanford University

• Stanford Seminar - The Human Factors of Formal Methods from Stanford University

• Stanford CS25: V4 I Aligning Open Language Models from Stanford University

• Stanford CS25: V4 I Demystifying Mixtral of Experts from Stanford University

• Stanford CS25: V4 I Transformers that Transform Well Enough to Support Near-Shallow Architectures from Stanford University

• Stanford CS25: V4 I From Large Language Models to Large Multimodal Models from Stanford University

• Stanford CS25: V4 I Hyung Won Chung of OpenAI from Stanford University

• Stanford Seminar - MS&E447: ZKP Panel with Dan Boneh, Jens Groth , Daniel Marin, and Ravi Mhatre from Stanford University

• MedAI #124: SleepFM: Multi-modal Representation Learning for Sleep | Rahul Thapa from Stanford University

• MedAI #121: HECTOR - Multimodal DL model for recurrence risk in endometrial cancer | Sarah Volinsky from Stanford University

• MedAI #120: Holistic OR Domain Modeling with Large Vision Language Models | Ege özsoy from Stanford University

• MedAI #119: AI-Driven Advancements in Mammogram Analysis | Aisha Urooj from Stanford University

• AI's Global Impact on Democracy and Governance from Stanford University

• Panel: AI’s Role in Improving Health Care Services in Our Communities from Stanford University

• AI Transforms Health Care | Artificial Intelligence: The Future of Medicine & Health Care Is Here from Stanford University

• Opening Remarks | Artificial Intelligence: The Future of Medicine & Health Care Is Here from Stanford University

• MedAI #126: Divide & Conquer - Concept-based Models for Efficient Transfer Learning | Shantanu Ghosh from Stanford University

• 투자 기술의 혁신: AI from University of Michigan

• 人、テクノロジー、そしてモビリティの未来 from University of Michigan

• 사람, 기술, 모빌리티의 미래 from University of Michigan

• 머신 러닝 기초: 사례 연구 접근 방식 from University of Washington

• 머신 러닝: 회귀 모델 from University of Washington

• Computer Science and IT: Faculty of Engineering and Information Technology Deep Dive from University of Melbourne

• The New DBfication of ML/AI from University of Melbourne

• Tractable novelty exploration over Continuous and Discrete Sequential Decision Problems from University of Melbourne

• Pioneering the Science of Information from University of Melbourne

• Convolutions in image processing | Week 1 | MIT 18.S191 Fall 2020 | Grant Sanderson from The Julia Programming Language

• Theory of Computation from Massachusetts Institute of Technology

• 6. The Structures of Power from Yale University

• Lessons from the regulatory process for medical software for image analysis and AI from Yale University

• Fast motion-resolved 4D MRI using convolutional networks without data consistency from Yale University

• Deep subspace learning for dynamic MR image reconstruction from Yale University

• Theoretical Interpretation of the ACID for Stabilizing Deep Reconstruction Networks from Yale University

• Fast uncertainty estimation and diffusion models in PET from Yale University

• Peter Carr Brooklyn Quant Experience (BQE) Seminar Series: Bruno Kamdem from New York University (NYU)

• Machine Learning for Personalised Healthcare: Opportunities, Challenges and Insights from New York University (NYU)

• Generative Models for Image Synthesis from New York University (NYU)

• Learning Representations Using Causal Invariance from New York University (NYU)

• Machine Learning for Personalization from New York University (NYU)

• The Information Knot Tying Sensing & Action; Emergence Theory of Representation Learning from New York University (NYU)

• Andrew Wiles talks to Hannah Fry from University of Oxford

• Apprivoiser l’apprentissage automatique from Université de Montréal

• ChatGPT pour tous from Université de Montréal

Data Science (221)

• R Programming from Johns Hopkins University ★★★☆☆(245)

• The Data Scientist’s Toolbox from Johns Hopkins University ★★★☆☆(166)

• Fundamentals of Qualitative Research Methods from Yale University ★★★★★(152)

• The Analytics Edge from Massachusetts Institute of Technology ★★★★★(80)

• Getting and Cleaning Data from Johns Hopkins University ★★★☆☆(58)

• Python for Data Science from University of California, San Diego ★★★★☆(48)

• Introduction to Data Science in Python from University of Michigan ★★☆☆☆(46)

• Introduction to Big Data from University of California, San Diego ★★★☆☆(34)

• Statistical Inference from Johns Hopkins University ★★★☆☆(34)

• Regression Models from Johns Hopkins University ★★★☆☆(33)

• Probability and Statistics in Data Science using Python from University of California, San Diego ★★☆☆☆(32)

• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(30)

• Reproducible Research from Johns Hopkins University ★★★★☆(27)

• A Crash Course in Data Science from Johns Hopkins University ★★★★☆(25)

• Hadoop Platform and Application Framework from University of California, San Diego ★★☆☆☆(25)

• Mining Massive Datasets from Stanford University ★★★★★(24)

• Data Visualization from University of Illinois at Urbana-Champaign ★★★☆☆(21)

• Pattern Discovery in Data Mining from University of Illinois at Urbana-Champaign ★★☆☆☆(21)

• Statistics and R from Harvard University ★★★★☆(20)

• Finding Hidden Messages in DNA (Bioinformatics I) from University of California, San Diego ★★★★★(18)

• Developing Data Products from Johns Hopkins University ★★★★☆(17)

• Python for Genomic Data Science from Johns Hopkins University ★★★☆☆(15)

• DCO042 - Python For Informatics from University of Michigan ★★★★★(14)

• Building a Data Science Team from Johns Hopkins University ★★★★☆(14)

• Data Science: R Basics from Harvard University ★★★★★(12)

• Data Science in Real Life from Johns Hopkins University ★★★☆☆(12)

• Next in Data Visualization | Michelle Borkin || Radcliffe Institute from Harvard University ★★★★☆(12)

• Biology Meets Programming: Bioinformatics for Beginners from University of California, San Diego ★★★☆☆(11)

• Text Mining and Analytics from University of Illinois at Urbana-Champaign ★★★★☆(10)

• Managing Data Analysis from Johns Hopkins University ★★★☆☆(9)

• Applied Plotting, Charting & Data Representation in Python from University of Michigan ★★☆☆☆(9)

• Foundations of Data Analysis - Part 1: Statistics Using R from The University of Texas at Austin ★★★★☆(8)

• Quantitative Methods from University of Amsterdam ★★★★★(7)

• Cluster Analysis in Data Mining from University of Illinois at Urbana-Champaign ★★★☆☆(7)

• Data Analysis Essentials from Imperial College London ★☆☆☆☆(7)

• Introduction to Systematic Review and Meta-Analysis from Johns Hopkins University ★★★★★(6)

• Graph Analytics for Big Data from University of California, San Diego ★★★☆☆(6)

• Algorithms for Big Data (COMPSCI 229r) from Harvard University ★★★★★(6)

• Computing for Data Analysis from Georgia Institute of Technology ★★★☆☆(6)

• Data Science: Visualization from Harvard University ★★★★★(5)

• Genome Sequencing (Bioinformatics II) from University of California, San Diego ★★★★★(5)

• People Analytics from University of Pennsylvania ★★★★☆(5)

• Quantitative Biology Workshop from Massachusetts Institute of Technology ★★★★★(5)

• Introduction to Analytics Modeling from Georgia Institute of Technology ★★★★☆(5)

• Big data and Language 1 from Korea Advanced Institute of Science and Technology ★★★★☆(5)

• Qualitative Research Methods from University of Amsterdam ★★★★☆(5)

• Data Manipulation at Scale: Systems and Algorithms from University of Washington ★★★☆☆(4)

• STAT115 2020 from Harvard University ★★★★★(4)

• Data Science Capstone from Johns Hopkins University ★★★☆☆(4)

• Stanford Seminar - Secure Data Science on the Internet of Things from Stanford University ★★★★☆(4)

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions from Harvard University ★★★★★(3)

• Big data and Language 2 from Korea Advanced Institute of Science and Technology ★★★★★(3)

• Big Data Integration and Processing from University of California, San Diego ★★★★☆(3)

• Data Science: Machine Learning from Harvard University ★★★★☆(3)

• High-Dimensional Data Analysis from Harvard University ★★★★☆(3)

• Applied Social Network Analysis in Python from University of Michigan ★★★★☆(3)

• Framework for Data Collection and Analysis from University of Maryland, College Park ★★★★☆(3)

• The R Programming Environment from Johns Hopkins University ★★★☆☆(3)

• Big Data Modeling and Management Systems from University of California, San Diego ★★★☆☆(3)

• Introduction to Accounting Data Analytics and Visualization from University of Illinois at Urbana-Champaign ★★★★★(2)

• Comparing Genes, Proteins, and Genomes (Bioinformatics III) from University of California, San Diego ★★★★★(2)

• Data Analysis: Statistical Modeling and Computation in Applications from Massachusetts Institute of Technology ★★★★★(2)

• Data Science: Probability from Harvard University ★★★★☆(2)

• Data Science: Wrangling from Harvard University ★★★★★(2)

• Genomic Data Science and Clustering (Bioinformatics V) from University of California, San Diego ★★★★☆(2)

• Applied Text Mining in Python from University of Michigan ★★☆☆☆(2)

• Research Data Management and Sharing from The University of North Carolina at Chapel Hill ★★★★☆(2)

• Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘：理论与算法 from Tsinghua University ★★★☆☆(2)

• Communicating Data Science Results from University of Washington ★☆☆☆☆(2)

• Executive Data Science Capstone from Johns Hopkins University ★★★★★(2)

• The Analytics Edge (Spring 2017) from Massachusetts Institute of Technology ★★★★★(2)

• Stanford Seminar - When DNA Meets AI from Stanford University ★★★★★(2)

• Stanford Seminar - Data Analytics at the Exascale for Free Electron Lasers Project from Stanford University ★★★★☆(2)

• Stanford Seminar - New Platforms for Development Solutions from Stanford University ★★★★★(2)

• Data Analytics in Health – From Basics to Business from KU Leuven University ★★★★★(1)

• Advanced Linear Models for Data Science 1: Least Squares from Johns Hopkins University ★★★★★(1)

• Data Science: Productivity Tools from Harvard University ★★★★★(1)

• Advanced Linear Models for Data Science 2: Statistical Linear Models from Johns Hopkins University ★★★★★(1)

• Data Science: Inference and Modeling from Harvard University ★★★★☆(1)

• Big Data and Education from Columbia University ★★★☆☆(1)

• Advanced R Programming from Johns Hopkins University ★★★★☆(1)

• 3D Data Visualization for Science Communication from University of Illinois at Urbana-Champaign ★★★★★(1)

• Data Analytics Foundations for Accountancy I from University of Illinois at Urbana-Champaign ★☆☆☆☆(1)

• AI Skills for Engineers: Data Engineering and Data Pipelines from Delft University of Technology ★★★★☆(1)

• Policy Analysis Using Interrupted Time Series from The University of British Columbia ★★★★★(1)

• Basic Data Processing and Visualization from University of California, San Diego ★★★☆☆(1)

• Big Data Analysis with Scala and Spark from École Polytechnique Fédérale de Lausanne ★★★☆☆(1)

• Introduction to Data Science with Python from Harvard University ★★★★☆(1)

• Bioinformatics: Introduction and Methods 生物信息学: 导论与方法 from Peking University ★★★★★(1)

• Building R Packages from Johns Hopkins University ★☆☆☆☆(1)

• Bioinformatics Capstone: Big Data in Biology from University of California, San Diego ★☆☆☆☆(1)

• Transforming with Data Analytics and Organization from University of Maryland, College Park ★★★★★(1)

• Storytelling and Persuading with Data and Digital Technology from University of Maryland, College Park ★★★★★(1)

• Data Science for Construction, Architecture and Engineering from National University of Singapore

• Principles, Statistical and Computational Tools for Reproducible Data Science from Harvard University

• Data Science: Capstone from Harvard University

• Quantitative and Qualitative Research for Beginners from National University of Singapore

• Data Analysis Using Python from University of Pennsylvania

• Data Analysis and Visualization from Georgia Institute of Technology

• Molecular Evolution (Bioinformatics IV) from University of California, San Diego

• Introduction to Genomic Data Science from University of California, San Diego

• Big Data Analytics in Healthcare from Georgia Institute of Technology

• Design Thinking and Predictive Analytics for Data Products from University of California, San Diego

• Finding Mutations in DNA and Proteins (Bioinformatics VI) from University of California, San Diego

• Tools for Exploratory Data Analysis in Business from University of Illinois at Urbana-Champaign

• Plant Bioinformatics from University of Toronto

• Foundations of Sports Analytics: Data, Representation, and Models in Sports from University of Michigan

• Data Visualization for Genome Biology from University of Toronto

• Statistical Computing with R - a gentle introduction from University College London

• Data Mining Project from University of Illinois at Urbana-Champaign

• Building Data Visualization Tools from Johns Hopkins University

• Applying Data Analytics in Accounting from University of Illinois at Urbana-Champaign

• Data Analytics for Business from Georgia Institute of Technology

• Health Data Science Foundation from University of Illinois at Urbana-Champaign

• Data Use for Disease Control & Global Health Decision-Making from Johns Hopkins University

• Big Data Analysis with Scala and Spark (Scala 2 version) from École Polytechnique Fédérale de Lausanne

• Dealing With Missing Data from University of Maryland, College Park

• Foundations of Data Analytics from The Hong Kong University of Science and Technology

• Code Free Data Science from University of California, San Diego

• Wearable Technologies and Sports Analytics from University of Michigan

• Graph Algorithms in Genome Sequencing from University of California, San Diego

• Dynamic Programming: Applications In Machine Learning and Genomics from University of California, San Diego

• Data Creation and Collection for Artificial Intelligence via Crowdsourcing from Delft University of Technology

• Combining and Analyzing Complex Data from University of Maryland, College Park

• Exploratory Data Analysis from Johns Hopkins University

• Data Science in Stratified Healthcare and Precision Medicine from University of Edinburgh

• Algorithms and Data Structures Capstone from University of California, San Diego

• Collaborative Data Science for Healthcare from Massachusetts Institute of Technology

• Prediction Models with Sports Data from University of Michigan

• Sampling People, Networks and Records from University of Michigan

• Data – What It Is, What We Can Do With It from Johns Hopkins University

• Computational Reasoning with Microsoft Excel from National University of Singapore

• Digital Transformation with Data Analytics Projects from University of Maryland, College Park

• Hacking COVID-19 — Course 2: Decoding SARS-CoV-2's Secrets from University of California, San Diego

• Data Mining and Knowledge Discovery from The Hong Kong University of Science and Technology

• Design Computing: 3D Modeling in Rhinoceros with Python/Rhinoscript from University of Michigan

• Plant Bioinformatics Capstone from University of Toronto

• Arranging and Visualizing Data in R from University of Michigan

• Big Data Computing with Spark from The Hong Kong University of Science and Technology

• Data Analysis: Building Your Own Business Dashboard from Delft University of Technology

• Big Data Analytics Using Spark from University of California, San Diego

• Making Evidence-Based Strategic Decisions from University of Maryland, College Park

• Big Data for Reliability and Security from Purdue University

• Big Data - Capstone Project from University of California, San Diego

• Advanced Big Data Systems | 高级大数据系统 from Tsinghua University

• Politics and Ethics of Data Analytics in the Public Sector from University of Michigan

• Design Strategies for Maximizing Total Data Quality from University of Michigan

• Mathematical Methods for Data Analysis from The Hong Kong University of Science and Technology

• Moneyball and Beyond from University of Michigan

• Doing Economics: Measuring Climate Change from University of London International Programmes

• Fondamentaux de la science des données from Université de Montréal

• Measuring Total Data Quality from University of Michigan

• The Total Data Quality Framework from University of Michigan

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Mastering Software Development in R Capstone from Johns Hopkins University

• Data Visualization Capstone from Johns Hopkins University

• Measurement – Turning Concepts into Data from Johns Hopkins University

• Quantifying Relationships with Regression Models from Johns Hopkins University

• Science des données et santé from Université de Montréal

• Fundamentals of Data Analytics in the Public Sector with R from University of Michigan

• Data Literacy Capstone – Evaluating Research from Johns Hopkins University

• STAT115 2018 from Harvard University

• STAT115 2019 from Harvard University

• Data Science: A New Way of Thinking | 数据科学导论 from Tsinghua University

• مجموعة أدوات عالم البيانات from Johns Hopkins University

• How to Describe Data from University of Michigan

• Gestion de l’analyse des données from Johns Hopkins University

• الحصول على البيانات وتنظيفها from Johns Hopkins University

• 生物信息学: 导论与方法 from Peking University

• MIT Deep Learning in Life Sciences 6.874 Spring 2020 from Massachusetts Institute of Technology

• MIT CompBio Course Projects Fall 2019 from Massachusetts Institute of Technology

• تحليل البيانات الاستكشافية from Johns Hopkins University

• Stanford Seminar - Theories of inference for visual analysis from Stanford University

• مقدمة عن البيانات الضخمة from University of California, San Diego

• 医学统计学与SPSS软件（基础篇） from Peking University

• Mining Online Data Across Social Networks from Stanford University

• Exploration et production de données pour les entreprises from University of Illinois at Urbana-Champaign

• 人群与网络 from Peking University

• Cours intensif sur la science des données from Johns Hopkins University

• Análisis de datos: Diseño y Visualización de Tableros from Delft University of Technology

• Stanford Seminar - Towards theories of single-trial high dimensional neural data analysis from Stanford University

• Stanford Seminar - Big Data is (at least) Four Different Problems from Stanford University

• Stanford Seminar - TSAR (the TimeSeries AggregatoR) Anirudh Todi of Twitter from Stanford University

• Big Data Analytics from California Institute of Technology

• Data Mining: The Tool of The Information Age from Stanford University

• Stanford Webinar - How to Analyze Research Data: Kristin Sainani from Stanford University

• Stanford Seminar - Developing Design Spaces for Visualization - Tamara Munzner from Stanford University

• Gestión del análisis de datos from Johns Hopkins University

• Soccermatics: could a Premier League team one day be managed by a mathematician? from University of Oxford

• Introduction to R and Geographic Information Systems (GIS) from Massachusetts Institute of Technology

• Mathematics of Big Data and Machine Learning from Massachusetts Institute of Technology

• Análisis de datos: Llévalo al MAX() from Delft University of Technology

• Управление анализом данных from Johns Hopkins University

• Bioinformatics Methods for Transcriptomics from Johns Hopkins University

• Muestreo de personas, redes y registros from University of Michigan

• Ein Crashkurs in Datenwissenschaft from Johns Hopkins University

• Datenanalyse verwalten from Johns Hopkins University

• Cómo combinar y analizar datos complejos from University of Maryland, College Park

• Stanford Seminar - Forecasting and Predicting the Future of the Future from Stanford University

• Stanford Seminar - Jupyter Notebooks and Academic Publication from Stanford University

• Stanford Seminar - The End of Privacy from Stanford University

• Der Werkzeugkasten des Data Scientist from Johns Hopkins University

• Cómo manejar datos faltantes from University of Maryland, College Park

• Data Analysis for Social Scientists from Massachusetts Institute of Technology

• Method of Detection: The Critical Missing Link in U.S. Cancer Registries from Yale University

• Big Data's Big Deal - Viktor Mayer-Schonberger from University of Oxford

• Stanford Seminar - Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization from Stanford University

• Stanford Seminar - Mobilizing the Future from Stanford University

• Stanford Seminar: Data For The People, Andreas Weigend of Social Data Lab from Stanford University

• EngX: Big Data, Big Impact mini-conference, Russ Altman, Jure Leskovec and Christopher Ré from Stanford University

• Stanford Seminar - Zhang Lin on MobileUrban Sensing in Beijing from Stanford University

• Stanford Seminar - Human-Machine Symbiosis in Data Visualization from Stanford University

• Stanford Seminar - Harnessing Data for Social Impact from Stanford University

• 파이썬의 데이터 과학 소개 from University of Michigan

• 파이썬의 응용 소셜 네트워크 분석 from University of Michigan

• 데이터 과학자의 도구 상자 from Johns Hopkins University

• Next in Data Visualization | Arvind Satyanarayan || Radcliffe Institute from Harvard University

• Communicating Complex Statistical Ideas to the Public: Lessons from the Pandemic - D. Spiegelhalter from University of Oxford

Information Security (InfoSec) (62)

• CS 253 Web Security from Stanford University ★★★★★(40)

• Computer Systems Security from Massachusetts Institute of Technology ★★★★★(36)

• Web Security Fundamentals from KU Leuven University ★★★★☆(21)

• Stanford Seminar - Engineering Cyber Resiliency: A Pragmatic Approach from Stanford University ★★★★★(17)

• Stanford Webinar - Hash, Hack, Code: Emerging Trends in Cyber Security from Stanford University ★★★★★(9)

• Intro to Information Security from Georgia Institute of Technology ★★☆☆☆(2)

• Network Security from Georgia Institute of Technology ★★★★★(1)

• Introduction to Cyber Attacks from New York University (NYU) ★★★★★(1)

• CS50's Introduction to Cybersecurity from Harvard University ★★★★☆(1)

• Cybersecurity for Everyone from University of Maryland, College Park ★★★★★(1)

• Cyber Security Economics from Delft University of Technology ★★☆☆☆(1)

• Real-Time Cyber Threat Detection and Mitigation from New York University (NYU)

• Introduction to Cybersecurity from University of Washington

• Cyber Attack Countermeasures from New York University (NYU)

• Cyber-Physical Systems Security from Georgia Institute of Technology

• Building a Cybersecurity Toolkit from University of Washington

• Cybersecurity: The CISO's View from University of Washington

• Finding Your Cybersecurity Career Path from University of Washington

• Enterprise and Infrastructure Security from New York University (NYU)

• Introduction to DevSecOps from Johns Hopkins University

• A Strategic Approach to Cybersecurity from University of Maryland, College Park

• Cybersecurity Capstone Project from University of Maryland, College Park

• Leadership from Infosec

• Mobile Payment Security from New York University (NYU)

• La cybersécurité en milieu universitaire from Université de Montréal

• Stanford Webinar - Securing the World Around Us: Cyber Security for the Physical Economy from Stanford University

• The Growing Threat and Impact of Web-Based Malware - Stanford Computer Security from Stanford University

• Stanford Seminar - The Current State of Cybersecurity from Stanford University

• Stanford Seminar - Solving Cybersecurity as an Economic Problem from Stanford University

• Security Challenges in 5G Wireless and Beyond from New York University (NYU)

• Webinar - Big Breaches: What We Learned From the World’s Most Disruptive Cybersecurity Attacks from Stanford University

• Taking Memory Forensics to the Next Level from New York University (NYU)

• Stanford Seminar - Preventing Successful Cyberattacks Using Strongly-typed Actors from Stanford University

• Stanford Webinar: Building Your Shield: Mapping the Cybersecurity Market, Dan Boneh and Neil Daswani from Stanford University

• Stanford Seminar - Tales from the Risks Forum from Stanford University

• Stanford Webinar: To Attribute or Not Attribute, Is That the Question? from Stanford University

• Stanford Seminar - Locking the Web Open--a Call for a New, Decentralized Web from Stanford University

• Stanford Seminar - Online Political Ad Transparency from Stanford University

• Stanford Seminar - Computer Security: The Mess We're In, How We Got Here, and What to Do About It from Stanford University

• Stanford Seminar - Thunderclap & CHERI (Capability Hardware-Enhanced RISC Instructions) from Stanford University

• Bulletproofs: Short Proofs for Confidential Transactions and More from Stanford University

• Stanford Seminar - Lessons from Mirai and the Current State of IoT Security from Stanford University

• Stanford Seminar - Exploiting modern microarchitectures: Meltdown, Spectre, & other hardware attacks from Stanford University

• Stanford Seminar - RowHammer, RowPress and Beyond: Can We Be Free of Bitflips (Soon)? from Stanford University

• Stanford Seminar - How Can Privacy Exist in a Data-Driven World? from Stanford University

• 디지털 민주주의의 명암 from University of Michigan

• Proving confidentiality and its preservation for mixed-sensitivity concurrent programs from University of Melbourne

• Industry Insights: Cyber Security from University of Melbourne

• A conversation with a recent Tandon Cyber Fellow grad, Michael Leking (Tandon '21) from New York University (NYU)

• Anatomy of an Attack: What Really Happens and How To Protect Your Enterprise from New York University (NYU)

• Trading Privacy for Convenience in the Age of Technology: Part 2 from New York University (NYU)

• CSAW'21 Keynote | Cybersecurity: Keeping the Lights On, Dr. Martin Otto, Siemens Technology from New York University (NYU)

• IC Layout Security from New York University (NYU)

• Conversations at the Forefront of Cybersecurity with NYU CCS featuring Nasir Memon from New York University (NYU)

• Conversations at the Forefront of Cyber Security with NYU CCS featuring Joel Caminer from New York University (NYU)

• Conversations at the Forefront of Cybersecurity with NYU CCS featuring Randy Milch from New York University (NYU)

• Conversations at the Forefront of Cyber Security with NYU CCS featuring Dr. Justin Cappos from New York University (NYU)

• Conversations at the Forefront of Cyber Security with NYU CCS featuring Dr. Damon McCoy from New York University (NYU)

• Dispelling the Top 10 Myths in Cybersecurity from New York University (NYU)

• 9th Cyber Security Lecture Sponsored by AIG from New York University (NYU)

• 8. Ryan Stortz & Kareem El-Faramawi - Firing Rounds at the Analysis Shooting Gallery from New York University (NYU)

• 2. Andrew Dutcher - angr from New York University (NYU)