[2025] 1000+ Free Computer Science Courses from World’s Top Universities

In this article, we’ve compiled 1000+ online courses offered by the 60 best universities in the world for studying computer science in 2025.

Manoel first built the list in 2020 using a data-driven approach that we have used each year, including 2025. You can find the methodology below. But if you’d rather go straight to the course list, click here.

Methodology

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

For the 2025 update, Suparn used a similar methodology to find the top 60 universities offering online computer science courses.

Here are the rankings used:

• QS: World University Ranking 2025 — Computer Science & IT
• Times Higher Education: World University Ranking 2025 — Computer Science.

Then, we crawled and scraped each ranking.

Now that we had some data, we used Jupyter with Python to process it. We combined the two 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.

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

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 1000+ online courses offered by the 60 best universities in the world for studying computer science in 2025.

Stats

• Enrollments range from 10 to over 13 million, with 14 courses exceeding 1 million enrollments
• Altogether, they have over 81M enrollments, with an average of 222K enrollments
• 921 courses are in English, 114 Chinese, 5 Spanish, and 3 French
• Together, they account for more than 78K reviews at Class Central, with an average of 216 reviews
• Average rating: 4.37 out of 5.0
• All these courses are free or can be audited for free
• 104 courses are beginner level, 111 are intermediate level, and 49 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 1000 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:

• 2000+ Free Developer and IT Certifications
• Harvard Computer Science Courses with Free Certificate
• 40+ Free Certificates from Wolfram U
• 800+ Free Certificates: Master Tech & Soft Skills with CodeSignal Learn
• 10,000+ Free Courses from Tech Giants: Learn from Google, Microsoft, Amazon, and More
• 8000+ OpenCourseWare Courses from Top Institutions.

Subjects

• Computer Science (727)
• Programming (151)
• Data Science (110)
• Information Security (InfoSec) (55)

Computer Science (727)

• Harvard CS50 – Full Computer Science University Course from Harvard University ★★★★★(1012)
• CS50’s Introduction to Computer Science from Harvard University ★★★★★(204)
• Artificial Intelligence from Massachusetts Institute of Technology ★★★★★(182)
• Introduction to Electrical Engineering and Computer Science I from Massachusetts Institute of Technology ★★★★★(58)
• Advanced Algorithms – COMPSCI 224 from Harvard University ★★★★★(35)
• Introduction to Artificial Intelligence from Stanford University ★★★★☆(31)
• Machine Learning with Python: from Linear Models to Deep Learning. from Massachusetts Institute of Technology ★★★☆☆(30)
• CS50’s Introduction to Artificial Intelligence with Python from Harvard University ★★★★★(29)
• Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues from Georgia Institute of Technology ★★★★★(28)
• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★★(25)
• CS50’s Computer Science for Business from Harvard University ★★★★★(21)
• Intro to Machine Learning from Stanford University ★★★★☆(20)
• Automata Theory from Stanford University ★★★★☆(20)
• Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★★(20)
• Computer Science 101 from Stanford University ★★★★☆(19)
• Cryptocurrency Engineering and Design from Massachusetts Institute of Technology ★★★★★(19)
• Human-Computer Interaction I: Fundamentals & Design Principles from Georgia Institute of Technology ★★★★★(15)
• How to Code: Simple Data from The University of British Columbia ★★★★☆(15)
• Design of Computer Programs from Stanford University ★★★★☆(14)
• Unix Tools: Data, Software and Production Engineering from Delft University of Technology ★★★★★(13)
• AI for Clinical Trials and Precision Medicine – Ruishan Liu from Stanford University ★★★★★(11)
• Learning from Data (Introductory Machine Learning course) from California Institute of Technology ★★★★★(10)
• CS50’s Computer Science for Lawyers from Harvard University ★★★★★(8)
• Information and Communication Technology (ICT) Accessibility from Georgia Institute of Technology ★★★★☆(8)
• Reinforcement Learning from Brown University ★★★☆☆(8)
• Machine Learning Fundamentals from University of California, San Diego ★★★★☆(8)
• Introduction to Computer Science and Programming in Python from Massachusetts Institute of Technology ★★★★★(8)
• Machine Learning from Georgia Institute of Technology ★★★★☆(7)
• Introduction to Computer Vision from Georgia Institute of Technology ★★★★★(7)
• Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps from Georgia Institute of Technology ★★★★★(7)
• Performance Engineering of Software Systems from Massachusetts Institute of Technology ★★★★★(7)
• Convolutional Neural Networks for Visual Recognition from Stanford University ★★★★☆(7)
• Advanced Operating Systems from Georgia Institute of Technology ★★★★★(6)
• Introduction to Computer Architecture from Carnegie Mellon University ★★★★★(6)
• Computer Networking from Georgia Institute of Technology ★★★★☆(6)
• Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms from Georgia Institute of Technology ★★★★★(6)
• Machine Learning for Healthcare from Massachusetts Institute of Technology ★★★★★(6)
• MIT 6.824 Distributed Systems – Spring 2020 from Massachusetts Institute of Technology ★★★★★(6)
• Data Structures: An Active Learning Approach from University of California, San Diego ★★★★★(5)
• How to Code: Complex Data from The University of British Columbia ★★★★★(5)
• Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms from Georgia Institute of Technology ★★★★★(5)
• Distributed Systems from University of Cambridge ★★★★★(5)
• Electrical and Computer Engineering – Systems Programming and Concurrency from University of Waterloo ★★★★☆(5)
• Human-Computer Interaction II: Cognition, Context & Culture from Georgia Institute of Technology ★★★★★(4)
• 6.S191: Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★☆(4)
• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(4)
• AI and Education Summit: Transforming Learning and Workforce Readiness from Stanford University ★★★★★(4)
• Software Architecture & Design from Georgia Institute of Technology ★★★★★(3)
• Language, Proof and Logic from Stanford University ★★★★☆(3)
• Machine Learning: Unsupervised Learning from Brown University ★★★☆☆(3)
• Algorithmic Design and Techniques from University of California, San Diego ★★★★☆(3)
• Using Electronic Health Records for Better Care from Stanford University ★★★★★(3)
• Design & AI – Envisioning AI-Augmented Products from Stanford University ★★★★★(3)
• Stanford Seminar – Security and the Software Defined Network from Stanford University ★★★★☆(3)
• Stanford Seminar – Self-Driving Cars for Everyone from Stanford University ★★★★★(3)
• Stanford Seminar – Practical Blockchain Applications – Steven Pu from Stanford University ★★★★★(3)
• Stanford Webinar – IOT: From Smart Sensors to Smart Networks from Stanford University ★★★★★(3)
• Stanford Seminar – Gender Disparities in Software Engineering from Stanford University ★★★★☆(3)
• Introduction to Operating Systems from Georgia Institute of Technology ★★★★★(2)
• Human-Computer Interaction IV: Evaluation, Agile Methods & Beyond from Georgia Institute of Technology ★★★★★(2)
• Deep Learning for Natural Language Processing from University of Oxford ★★★★★(2)
• Software Engineering: Introduction from The University of British Columbia ★★★☆☆(2)
• Knowledge-Based AI: Cognitive Systems from Georgia Institute of Technology ★★★☆☆(2)
• The Quantum Internet and Quantum Computers: How Will They Change the World? from Delft University of Technology ★★★☆☆(2)
• 6.S094: Deep Learning for Self-Driving Cars from Massachusetts Institute of Technology ★★★★☆(2)
• Introduction to Algorithms (SMA 5503) from Massachusetts Institute of Technology ★★★★★(2)
• Introduction to Computer Science and Programming (Fall 2008) from Massachusetts Institute of Technology ★★★★★(2)
• Stanford – Human-Computer Interaction Seminar Series from Stanford University ★★★★★(2)
• How to Compute with Schrödinger’s Cat – An Introduction to Quantum Computing from Stanford University ★★★★☆(2)
• Web3 Considered – Possible Futures for Decentralization and Digital Ownership from Stanford University ★★★★☆(2)
• Race, Technology, and Algorithmic Bias – Vision and Justice from Harvard University ★★★★★(2)
• Deep Learning Lecture Series from University College London ★★★★★(2)
• Towards Unsupervised Biomedical Image Segmentation Using Hyperbolic Representations – Jeffrey Gu from Stanford University ★★★★☆(2)
• Creating Human-Computer Partnerships – Stanford Seminar from Stanford University ★★★★★(2)
• Stanford Seminar – Decision Transformer: Reinforcement Learning via Sequence Modeling from Stanford University ★★★★★(2)
• Computability, Complexity & Algorithms from Georgia Institute of Technology ★★★★★(1)
• High Performance Computer Architecture from Georgia Institute of Technology ★★★★★(1)
• Health Informatics: A Current and Historical Perspective from Georgia Institute of Technology ★★★★★(1)
• Human-Computer Interaction III: Ethics, Needfinding & Prototyping from Georgia Institute of Technology ★★★★★(1)
• Compilers from Stanford University ★★★★★(1)
• Introduction to Graduate Algorithms from Georgia Institute of Technology ★★★★★(1)
• Software Construction: Object-Oriented Design from The University of British Columbia ★★☆☆☆(1)
• Introduction to Scientific Machine Learning from Purdue University ★★★★☆(1)
• Software Construction: Data Abstraction from The University of British Columbia ★★★☆☆(1)
• Machine Learning for Semiconductor Quantum Devices from Delft University of Technology ★★★★☆(1)
• Data Structures and Algorithm Design Part II | 数据结构与算法设计(下) from Tsinghua University ★★★★★(1)
• Introduction to Computational Science and Engineering from Massachusetts Institute of Technology ★★★★☆(1)
• Nature, in Code: Biology in JavaScript from École Polytechnique Fédérale de Lausanne ★★★☆☆(1)
• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★☆(1)
• Mathematics for Computer Science from Massachusetts Institute of Technology ★★★★☆(1)
• Design and Analysis of Algorithms from Massachusetts Institute of Technology ★★★★★(1)
• Introduction to Computer Science and Programming (Spring 2011) from Massachusetts Institute of Technology ★★★★★(1)
• Boltzmann Law: Physics to Computing from Purdue University ★★★★☆(1)
• Deep Learning for Computer Vision from University of Michigan ★★★★★(1)
• Quantum Computer Systems Design III: Working with Noisy Systems from The University of Chicago ★★★★☆(1)
• Understanding the World Through Data from Massachusetts Institute of Technology ★★★★☆(1)
• Learning and Memory from Yale University ★★★★★(1)
• Beyond Cryptocurrency: Blockchain for the Real World from Stanford University ★★★★☆(1)
• Dive Into the World of Blockchain: Principles, Mechanics, and Tokens from University of Toronto ★★★☆☆(1)
• Rethinking the AI-UX Boundary for Designing Human-AI Experiences from Stanford University ★★★★★(1)
• How ChatGPT and Generative AI Will Shape the Future of Work 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)
• Tuning GPT-3 on a Single GPU via Zero-Shot Hyperparameter Transfer from Massachusetts Institute of Technology ★★★★★(1)
• MIT: Recurrent Neural Networks from Alexander Amini ★★★★★(1)
• Wireless Above 100GHz from New York University (NYU) ★★★☆☆(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)
• Synthetic Data Sets for the Financial Industry from Stanford University ★★★★☆(1)
• Learning Controllers – From Engineering to AGI from Massachusetts Institute of Technology ★★★★☆(1)
• Stanford Seminar – Google’s Multilingual Neural Machine Translation System from Stanford University ★★★★★(1)
• Deep Generative Modeling from Alexander Amini ★★★★★(1)
• Regulatory Evaluation of Image Processing Software Devices from Yale University ★★★★★(1)
• Edge Computing in Autonomous Vehicles – Panel Discussion from Stanford University ★★★★☆(1)
• Finding Fairness – Incorporating Societal Values in Computer Algorithms from Harvard University ★★★☆☆(1)
• Exploring the Implications of Machine Learning for Cognitive Disabilities from Stanford University ★★★★★(1)
• AI in Molecular Imaging from Yale University ★★★★★(1)
• Stanford Seminar – Toward Scalable Autonomy – Aleksandra Faust from Stanford University ★★★★☆(1)
• Pioneering the Science of Information – IBM’s Role in Shaping Information Technology from University of Melbourne ★★★★★(1)
• The Soul of a New Machine – Rethinking the Computer from Stanford University ★★★★☆(1)
• Data Compression I – Lecture 2: Prefix Free Codes from Stanford University ★★★★★(1)
• Feedback Control from Pixels from Massachusetts Institute of Technology ★★★☆☆(1)
• Visualization for Machine Learning – Google Brain from Alexander Amini ★★★★★(1)
• The Information Age and The Age of Creation from Stanford University ★★★★★(1)
• Algorithms: Design and Analysis, Part 1 from Stanford University
• Cyber-Physical Systems Design & Analysis from Georgia Institute of Technology
• Artificial Intelligence from Georgia Institute of Technology
• Introduction to Quantum Computing from The University of British Columbia
• Human-Computer Interaction from Georgia Institute of Technology
• Hacker Tools from Massachusetts Institute of Technology
• High Performance Computing from Georgia Institute of Technology
• Programming & Data Structures from Columbia University
• AI for Teacher Assistance from Georgia Institute of Technology
• Algorithms: Design and Analysis, Part 2 from Stanford University
• Introduction to Blockchain Technology and Applications from University College London
• Machine Learning and AI with Python from Harvard University
• AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning from Delft University of Technology
• Health Informatics: Data and Interoperability Standards from Georgia Institute of Technology
• Development and Applications of Germanium Quantum Technologies from Delft University of Technology
• Ethics in AI Design from Delft University of Technology
• Computer Applications of Artificial Intelligence and e-Construction from Purdue University
• AI skills for Engineers: Supervised Machine Learning from Delft University of Technology
• AI at the Edge on Arm: Understanding and Deploying LLMs for Mobile Devices from Arm Education
• Compilers: Theory and Practice from Georgia Institute of Technology
• Image Processing and Analysis for Life Scientists from École Polytechnique Fédérale de Lausanne
• Assessment Design with AI from Georgia Institute of Technology
• Introduction to Deep Learning from Purdue University
• Chatbots for Instruction from Georgia Institute of Technology
• Complex Problem Structuring: a Socio-Technical Perspective from Delft University of Technology
• Machine Learning from Georgia Institute of Technology
• Health Informatics: The Cutting Edge from Georgia Institute of Technology
• Computer Graphics II: Rendering from University of California, San Diego
• Computer Graphics from University of California, San Diego
• IoT Systems and Industrial Applications with Design Thinking from École Polytechnique Fédérale de Lausanne
• Introduction to Quantum Computing for Everyone from The University of Chicago
• Statistical Machine Learning from Carnegie Mellon University
• Fundamentals of Quantum Information from Delft University of Technology
• Applied Quantum Computing I: Fundamentals from Purdue University
• Accessible Gamification from Georgia Institute of Technology
• Applied Quantum Computing III: Algorithm and Software from Purdue University
• Data Structures Fundamentals from University of California, San Diego
• Computer Vision for Embedded Systems from Purdue University
• Graph Algorithms from University of California, San Diego
• Self-Driving Cars with Duckietown from ETH Zurich
• Machine Learning Use Cases in Finance from Université de Montréal
• Quantum Detectors and Sensors from Purdue University
• Fundamentals of TinyML from Harvard University
• Introduction to Quantum Computing for Everyone 2 from The University of Chicago
• Applications of Machine Learning in Plant Science from Cornell University
• Deep Learning Essentials from Université de Montréal
• Deploying TinyML from Harvard University
• Applied Quantum Computing II: Hardware from Purdue University
• MLOps for Scaling TinyML from Harvard University
• AI in Healthcare. Hype or Help? from KU Leuven University
• Quantum Communication and the Quantum Network Explorer from Delft University of Technology
• Quantum-safe Digital Infrastructures: Technical Challenges and Solutions from Delft University of Technology
• Applications of TinyML from Harvard University
• Quantum Computer Systems Design I: Intro to Quantum Computation and Programming from The University of Chicago
• Introduction à l’éthique de l’IA from Université de Montréal
• Bias and Discrimination in AI from Université de Montréal
• Explore Trading and Lending in Decentralized Finance from University of Toronto
• Quantum Computer Systems Design II: Principles of Quantum Architecture from The University of Chicago
• Data Structures and Algorithm Design Part I | 数据结构与算法设计(上) from Tsinghua University
• Quantum-safe Digital infrastructures: Challenges and Solutions for Governance from Delft University of Technology
• AI in Practice: Preparing for AI from Delft University of Technology
• LAFF-On Programming for High Performance from The University of Texas at Austin
• Modern Distributed Systems from Delft University of Technology
• GT – Refresher – Advanced OS from Georgia Institute of Technology
• Biais et discrimination en IA from Université de Montréal
• Techniques d’intelligence artificielle : des fondements aux applications from Université de Montréal
• The Society of Mind from Massachusetts Institute of Technology
• Introduction à l’apprentissage profond from Université de Montréal
• Advanced Data Structures from Massachusetts Institute of Technology
• NP-Complete Problems from University of California, San Diego
• String Processing and Pattern Matching Algorithms from University of California, San Diego
• Blockchain Technology and the Future of FinTech from University of Toronto
• Quantum Hardware and its Applications with Quantum Inspire from Delft University of Technology
• 计算几何 | Computational Geometry from Tsinghua University
• LAFF – On Programming for Correctness from The University of Texas at Austin
• L’essentiel de l’apprentissage profond from Université de Montréal
• Vision artificielle et exploitation intelligente des ressources naturelles from Université de Montréal
• Les coulisses des systèmes de recommandation from Université de Montréal
• Cryptocurrency Engineering and Design – Spring 2018 from Massachusetts Institute of Technology
• AI to Understand and Connect People from KU Leuven University
• AI in Practice: Applying AI from Delft University of Technology
• Big Data Machine Learning | 大数据机器学习 from Tsinghua University
• Introduction to Computers – Stanford University from Stanford University
• Navigating complex health data challenges from University of Cambridge
• Recommender Systems: Behind the Screen from Université de Montréal
• Navigating Decentralized Derivatives and Governance in Blockchain from University of Toronto
• How You Can Use ChatGPT to Increase Your Creative Output from Stanford University
• Optimization: principles and algorithms – Network and discrete optimization from École Polytechnique Fédérale de Lausanne
• Stanford CS229 – Machine Learning Full Course Taught by Andrew Ng – Autumn 2018 from Stanford University
• Deep Learning in Life Sciences – Spring 2021 from Massachusetts Institute of Technology
• 算法基础 | Fundamental Algorithms from Peking University
• Optimization: principles and algorithms – Unconstrained nonlinear optimization from École Polytechnique Fédérale de Lausanne
• Introduction to Computational Thinking from Massachusetts Institute of Technology
• Artificial Intelligence – Principles and Techniques – Autumn 2019 from Stanford University
• 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
• AI and Law: Implications for Society and Legal Systems from Stanford University
• Artificial Intelligence for Business Leaders from Stanford University
• Programming for the Puzzled (January IAP 2018) from Massachusetts Institute of Technology
• Stanford Seminar – Software-Defined Networking at the Crossroads from Stanford University
• Information Technology as an Integrating Force in Manufacturing from Massachusetts Institute of Technology
• Multicore Programming Primer from Massachusetts Institute of Technology
• 理论计算机科学基础 | Introduction to Theoretical Computer Science from Peking University
• Artificial Intelligence – Current and Future Paradigms and Implications from Stanford University
• Theory of Computation, Fall 2020 from Massachusetts Institute of Technology
• Cryptocurrencies and Blockchains – The Science Behind the Technology from Stanford University
• Introduction to CS and Programming using Python from Massachusetts Institute of Technology
• Stanford Seminar – Deep Learning for Medical Diagnoses from Stanford University
• Stanford Seminar – Transformers in Language: The Development of GPT Models Including GPT-3 from Stanford University
• Introduction to the Theory of Computing – Stanford from Stanford University
• Stanford Seminar – The FATE of AI Ethics, Anna Bethke from Stanford University
• Stanford Seminar – Designing Assistive Technologies for Agency from Stanford University
• Algorithmic Lower Bounds: Fun with Hardness Proofs from Massachusetts Institute of Technology
• Software-Centric Visible Light Communication for the Internet of Things from Stanford University
• How to Use ChatGPT to Increase Your Creative Output – Part Two from Stanford University
• MIT Introduction to Deep Learning from Alexander Amini
• Stanford CS330: Deep Multi-Task and Meta Learning from Stanford University
• Machine Learning Course from California Institute of Technology
• Using Data for Increased Realism with Haptic Modeling and Devices from Stanford University
• Stanford Seminar – Deep Learning in Speech Recognition from Stanford University
• The Future of Blockchain and Cryptocurrencies – Dan Boneh from Stanford University
• AI in the Workplace: Rethinking Skill Development from Stanford University
• AI Bias and Fairness from Alexander Amini
• Stanford Seminar – Natural Language Processing for Conversational Interfaces from Stanford University
• Stanford Seminar – Bugs in Crypto Implementations from Stanford University
• The Artful Design of Interactive AI Systems from Stanford University
• Artificial Intelligence Planning from University of Edinburgh
• Deep Generative Modeling from Alexander Amini
• MIT: Reinforcement Learning from Alexander Amini
• How AI Is Turning Your Smartphone Into the Swiss Army Knife of Clinical Diagnostics from Yale University
• Stanford CS234: Reinforcement Learning – Winter 2019 from Stanford University
• Stanford Seminar – Representation Learning for Autonomous Robots, Anima Anandkumar from Stanford University
• Stanford Seminar – Creating Interfaces with Rich Physical Properties Through Digital Fabrication from Stanford University
• How Machine Learning is Changing Software from Stanford University
• AI in Healthcare from Alexander Amini
• Machine Learning with Graphs – Fall 2019 from Stanford University
• Stanford Seminar – Enabling NLP, Machine Learning, and Few-Shot Learning Using Associative Processing from Stanford University
• Smart Physical Systems from the Standpoint of an AI Company from Stanford University
• Stanford Seminar – Can the Brain Do Back-Propagation? from Stanford University
• Frontiers of Medical AI – Therapeutics and Workflows from Stanford University
• Agentic AI: A Progression of Language Model Usage from Stanford University
• Data Augmentation for Image-Based Reinforcement Learning from Massachusetts Institute of Technology
• MIT EI Seminar – Phillip Isola – Emergent Intelligence- Getting More Out of Agents Than You Bake In from Massachusetts Institute of Technology
• MIT 6.S191 – Automatic Speech Recognition from Alexander Amini
• Theory of Computation from Massachusetts Institute of Technology
• 算法设计与分析(高级) | Advanced Design and Analysis of Algorithms from Peking University
• Deep Maths – Machine Learning and Mathematics from University of Oxford
• Forecasting and Aligning AI – Jacob Steinhardt from Stanford University
• Deep Learning for Symbolic Mathematics – Guillaume Lample & Francois Charton from Stanford University
• Stanford Seminar – Neural Networks on Chip Design from the User Perspective from Stanford University
• Solana Larsen- Who Has Power Over AI? from Stanford University
• Stanford Seminar – Rebooting the Internet from Stanford University
• Federated Learning in Medicine – Breaking Down Silos to Advance Medical Research from Stanford University
• Large Language Models – Will They Keep Getting Bigger? from Massachusetts Institute of Technology
• Jacob Andreas – Natural Language Explanations of Deep Networks from Massachusetts Institute of Technology
• Diverse Data and Efficient Algorithms for Robot Learning from Massachusetts Institute of Technology
• Towards AI for 3D Content Creation from Alexander Amini
• 6.036 Introduction to Machine Learning from Massachusetts Institute of Technology
• The Creativity Code – Marcus du Sautoy from University of Oxford
• Stanford Seminar – Training Classifiers with Natural Language Explanations from Stanford University
• Stanford Seminar – New Golden Age for Computer Architecture – John Hennessy from Stanford University
• Stanford Seminar – Applying Theory to Practice and Practice to Theory from Stanford University
• Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis – Max Lu from Stanford University
• Federated Hyperparameter Tuning – Challenges, Baselines, and Connections from Stanford University
• Stanford Seminar – What’s Next for Blockchain in China? from Stanford University
• Artificial Intelligence – The Future of Medicine and Health Care Is Here from Stanford University
• Neurosymbolic AI from Alexander Amini
• Beyond Deep Learning – Learning+Reasoning from Alexander Amini
• Sequence Modeling with Neural Networks from Alexander Amini
• Stanford Seminar – Lenia- Biology of Artificial Life, Bert Wang-Chak Chan from Stanford University
• Stanford Seminar – Scalable Intelligent Systems Build and Deploy by 2025 from Stanford University
• Stanford Seminar – Erudite: Prototype System for Computational Intelligence from Stanford University
• Stanford Seminar – Safe Passwords Made Easy to Use from Stanford University
• Computational Memory – A Stepping-Stone to Non-Von Neumann Computing? 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 – Citadel of One- Individuality and the Rise of the Machines, Suzanne Sadedin from Stanford University
• Dynamic Code Optimization and the NVIDIA Denver Processor from Stanford University
• Stanford Seminar – Instruction Sets Should Be Free- The Case for RISC-V from Stanford University
• Multimodal Opportunistic Risk Assessment for Ischemic Heart Disease – Juan Manuel from Stanford University
• Rethinking Architecture Design for Data Heterogeneity in FL – Liangqiong Qu from Stanford University
• Few-Shot Chest X-Ray Diagnosis Using Clinical and Literature Images from Stanford University
• Data-Efficient AI for Accelerating MRI Acquisition from Stanford University
• Hacking AI – Security & Privacy of Machine Learning Models from Stanford University
• Stanford CS229 – Machine Learning – Summer 2019 – Kernel Methods & Support Vector Machine from Stanford University
• Create a Better User Experience Through AI – Michael Bernstein from Stanford University
• NVIDIA GPU Computing – A Journey from PC Gaming to Deep Learning from Stanford University
• AI Transforms Health Care – The Future of Medicine and Health Care from Stanford University
• Computer Science and IT at the University of Melbourne – Faculty of Engineering and IT Overview from University of Melbourne
• CS50 2012 – Week 3 Continued: Search Algorithms and Sorting Techniques from Harvard University
• EI Seminar – Recent Papers in Embodied Intelligence from Massachusetts Institute of Technology
• Computational Principles Underlying the Learning of Sensorimotor Repertoires from Massachusetts Institute of Technology
• Intuitive Reasoning as Unsupervised Neural Generation from Massachusetts Institute of Technology
• The Lottery Ticket Hypothesis – Michael Carbin from Massachusetts Institute of Technology
• Recurrent Neural Networks and Transformers from Alexander Amini
• MIT: Introduction to Deep Learning from Alexander Amini
• Introduction to Deep Learning from Alexander Amini
• Advanced Topics in Cryptography from Massachusetts Institute of Technology
• Stanford Seminar – Failures & Where to Find Them: Considering Safety as a Function of Structure from Stanford University
• Denoising Diffusion Models for Denoising Diffusion MRI – Tiange Xiang from Stanford University
• Real-Time Seizure Detection Using EEG – Hyewon Jeong from Stanford University
• Deep Learning Methods for Electrocardiograms and Echocardiograms from Stanford University
• Towards Generalist Imaging Using Multimodal Self-Supervised Learning – Mars Huang from Stanford University
• The New Role of Physics Simulation in AI from Stanford University
• Stanford Seminar – Human-AI Interaction Under Societal Disagreement from Stanford University
• Inequality in Healthcare: Using AI and Data Science to Reduce Disparities from Stanford University
• Stanford Seminar – Taraxa.io: A Globalized Blockchain Protocol Startup from Stanford University
• Cybersecurity in the Modern Era – Zero Knowledge Proofs Explained from Stanford University
• Generative AI for Multimodal Biomedicine from Stanford University
• Building Embodied Autonomous Agents from Massachusetts Institute of Technology
• Machine Learning for Scent from Alexander Amini
• Introduction to Deep Learning from Alexander Amini
• Convolutional Neural Networks from Alexander Amini
• Convolutional Neural Networks from Alexander Amini
• Introduction to Deep Learning – MIT 2018 from Alexander Amini
• Improved Ultrasound Image Formation – Domain Adaptation With No Data from Yale University
• ChatGPT pour tous from Université de Montréal
• Stanford Seminar – Leela: A Semantic Intelligent Agent from Stanford University
• Stanford Seminar – An Architect’s Point of View on Emerging Technologies from Stanford University
• Stanford Seminar – Dialog Markets from Stanford University
• Stanford Seminar – Time Traveling Hardware and Software Systems from Stanford University
• Stanford Seminar – Concepts and Questions as Programs from Stanford University
• Voices in the Code – A Story About People, Their Values, and the Algorithm They Made from Stanford University
• Against Ethical Robots with Ron Chrisley from Stanford University
• Stanford Seminar – Is Google Search Dying? from Stanford University
• The Race for Technological Supremacy – Emerging Technologies and Global Implications from Stanford University
• Stanford Seminar – Fastai: A Layered API for Deep Learning from Stanford University
• Deep Learning – Class Introduction and Logistics – Lecture 1 from Stanford University
• How is AI Evolving? A Look at the Startup Landscape in the U.S. and Asia from Stanford University
• Entrepreneurial Lessons from Self-Driving Cars from Stanford University
• Stanford Seminar – Transformers United: Neuroscience-Inspired Artificial Intelligence from Stanford University
• Overview of Transformers – Stanford CS25: Lecture 1 from Stanford University
• Language Models as Temporary Training Wheels to Facilitate Learning from Stanford University
• Can You Trust a Computer Algorithm – How and Why from University of Melbourne
• Recent Papers in Embodied Intelligence I from Massachusetts Institute of Technology
• Deep Learning New Frontiers from Alexander Amini
• Deep Learning Limitations and New Frontiers from Alexander Amini
• Computer Vision Meets Social Networks from Alexander Amini
• MIT: Deep Reinforcement Learning from Alexander Amini
• MIT 6.S191 – Deep Learning Limitations and New Frontiers from Alexander Amini
• Modeling Prior Information to Guide Image Reconstruction in Radiation Therapy from Yale University
• Ideas for a Complex World – Anna Seigal from University of Oxford
• GGP Course Videos from Stanford University
• Stanford Seminar – Persistent and Unforgeable Watermarks for Deep Neural Networks from Stanford University
• Stanford Seminar – MIPS Open, Wave Computing from Stanford University
• Stanford Seminar – Efficient and Resilient Systems in the Cognitive Era from Stanford University
• Stanford Seminar – Distributed Perception and Learning Between Robots and the Cloud from Stanford University
• Stanford Seminar: Computational Ecosystems from Stanford University
• Stanford Seminar – HPC Opportunities in Deep Learning – Greg Diamos, Baidu from Stanford University
• Stanford Seminar – Mostly Missless Memory in the Mill CPU from Stanford University
• GANs in Medical Image Synthesis, Translation, and Augmentation – Jason Jeong from Stanford University
• Self-Training – Weak Supervision Using Untrained Neural Nets for MR Reconstruction – Beliz Gunel from Stanford University
• Segmentation and Quantification of Breast Arterial Calcifications – Xiaoyuan Guo from Stanford University
• How Can You Trust Machine Learning? from Stanford University
• Learning to See the Physical World from Stanford University
• Optimizing the Internet from Stanford University
• Lessons From Evaluating and Debugging Healthcare AI in Deployment from Stanford University
• How Can You Trust Machine Learning? from Stanford University
• Safety and Liveness of Robot Behaviors from Stanford University
• Stanford Seminar – Microprocessors from Stanford University
• Deep Learning Full-Cycle Projects – Lecture 3 from Stanford University
• Deep Learning Intuition – Lecture 2 from Stanford University
• Stanford Seminar – Virtual Human Agent for Smart City from Stanford University
• Leveraging Social Theories to Enhance Human-AI Interaction from Stanford University
• Twintrees, Baxter Permutations, and Floorplans in Computer Science – Lecture 2022 from Stanford University
• The State of Design Knowledge in Human-AI Interaction from Stanford University
• SleepFM – Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals from Stanford University
• AI’s Global Impact on Democracy and Governance from Stanford University
• AI’s Role in Improving Health Care Services in Our Communities from Stanford University
• Learn About: Google’s AI-Powered Learning Tool – HAI Seminar from Stanford University
• Accounting for Human Engagement Behavior to Enhance AI-Assisted Decision Making from Stanford University
• The Task Specification Problem from Massachusetts Institute of Technology
• Generalizable Autonomy for Robot Manipulation from Alexander Amini
• MIT 6.S191 – Recurrent Neural Networks from Alexander Amini
• Reinforcement Learning from Alexander Amini
• Convolutional Neural Networks from Alexander Amini
• Deep Generative Modeling from Alexander Amini
• Biologically Inspired Neural Networks from Alexander Amini
• Deep Generative Modeling from Alexander Amini
• Reinforcement Learning from Alexander Amini
• 计算机文化基础 from Tsinghua University
• AI and Industrial Innovation from Tsinghua University
• Apprivoiser l’apprentissage automatique from Université de Montréal
• Stanford Seminar – SMILE- Synchronized, Multi-sensory Integrated Learning Environment from Stanford University
• Instruction Execution on the Mill CPU from Stanford University
• Opioid Use Disorder Prediction Using AI and Existing Risk Models from Stanford University
• Explaining Model Decisions and Fixing Them Through Human Feedback from Stanford University
• Fairness in Representation Learning – Natalie Dullerud from Stanford University
• Generative Models With Domain Knowledge for Weakly Supervised Clustering from Stanford University
• Domain Adaptation with Invariant Representation Learning – What Transformations to Learn? from Stanford University
• Optimizing for Interpretability in Deep Neural Networks – Mike Wu from Stanford University
• Adversarial Debiasing With Partial Learning – Medical Image Studies from Stanford University
• MedAI – Training Medical Image Segmentation Models with Less Labeled Data – Sarah Hooper from Stanford University
• Observational Supervision for Medical Image Classification from Stanford University
• Multimodal Medical Research of Vision and Language – Jean-Benoit Delbrouck from Stanford University
• Style Transfer Augmentations for Computational Pathology – Rikiya Yamashita from Stanford University
• Learning the Structure of EHR with Graph Convolutional Transformer – Edward Choi from Stanford University
• 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 Seminar 2022 – Self Attention and Non-Parametric Transformers from Stanford University
• Stanford Seminar – Accelerating ML Recommendation with Over a Thousand RISC-V-Tensor Processors from Stanford University
• Stanford Seminar – Finding the Great Problems from Stanford University
• Deep Learning – Adversarial Attacks and GANs – Lecture 4 from Stanford University
• Information Theory of Deep Learning – Naftali Tishby from Stanford University
• Scoring News Articles to Fight Misinformation from Stanford University
• The Evolution of Public Key Cryptography from Stanford University
• Stanford Lecture – Don Knuth – “A Conjecture That Had To Be True” from Stanford University
• Stanford Seminar – Time Well Spent, Tristan Harris from Stanford University
• Stanford Seminar – Swiss Computer Systems from Stanford University
• Stanford Seminar – Challenges in Secure Messaging from Stanford University
• GPS for Humanity – Stanford Engineering Hero Lecture from Stanford University
• Algorithmic Governance: Auditing Search and Recommendation Algorithms for Problematic Content from Stanford University
• Beyond LLMs – Agents, Emergent Abilities, Intermediate-Guided Reasoning, and BabyLM – Stanford CS25 from Stanford University
• Stanford CS236: Deep Generative Models – Score Based Diffusion Models – Lecture 16 from Stanford University
• Deep Generative Models – Maximum Likelihood Learning – Lecture 4 from Stanford University
• Intuitions on Language Models and Shaping AI’s Future – Stanford CS25 Lecture from Stanford University
• The Human Factors of Formal Methods – Stanford Seminar from Stanford University
• From Large Language Models to Large Multimodal Models – Stanford CS25 – Lecture 4 from Stanford University
• When Design Equals Planning – Computational Approaches to Robot Design from Stanford University
• Holistic OR Domain Modeling with Large Vision Language Models – MedAI #120 from Stanford University
• AI-Driven Advancements in Mammogram Analysis – MedAI #119 from Stanford University
• Role of Instruction-Tuning and Prompt Engineering in Clinical Domain – MedAI 125 from Stanford University
• Large Scale Multi-Microscope Datasets and Their Challenges in Medical AI from Stanford University
• Open-world Segmentation and Tracking in 3D from Stanford University
• Me-LLaMA – Medical Foundation Large Language Models for Comprehensive Text Analysis and Beyond from Stanford University
• Creating Fair, Useful, and Reliable AI in Healthcare from Stanford University
• AI and Access to Justice from Stanford University
• Large Language Models in 2025 – How Much Understanding and Intelligence? from Stanford University
• Using Far from Perfect ML to Help Patients – A Billion Medical Devices from Stanford University
• Public AI Assistant to Worldwide Knowledge – Performing Interactive Tasks Under Developer Control from Stanford University
• Information, Calcul, Communication: Introduction à la pensée informatique from École Polytechnique Fédérale de Lausanne
• Getting Robust – Securing Neural Networks Against Adversarial Attacks from University of Melbourne
• Convolutional Neural Networks from Alexander Amini
• Deep Learning New Frontiers from Alexander Amini
• Evidential Deep Learning and Uncertainty from Alexander Amini
• Deep CPCFG for Information Extraction from Alexander Amini
• Taming Dataset Bias via Domain Adaptation from Alexander Amini
• MIT 6.S191 – Recurrent Neural Networks from Alexander Amini
• Lessons From the Regulatory Process for Medical Software for Image Analysis and AI from Yale University
• Model-Based Deep Learning – Beyond Unrolling from Yale University
• 区块链和加密数字货币 from Tsinghua University
• 软件工程 from Tsinghua University
• Data Structures and Algorithm Design Part I from Tsinghua University
• AI and Ethical Governance from Tsinghua University
• A Reinforcement Learning Mechanism for Trading Wind Power Futures from New York University (NYU)
• The Evolution of Public Key Cryptography from New York University (NYU)
• The Era of Artificial Intelligence from New York University (NYU)
• Statistical Physics, Neural Networks, and Neuroscience: From Then to Now from New York University (NYU)
• Information Theory: Huffman and Arithmetic Codes – Lecture 7 from University of Oxford
• Actionable Machine Learning for Tackling Distribution Shift – Huaxiu Yao from Stanford University
• Efficiently Modeling Long Sequences with Structured State Spaces – Albert Gu from Stanford University
• Assignment Control Plots for Causal Inference Study Design – Rocky Aikens from Stanford University
• Mandoline – Model Evaluation under Distribution Shift from Stanford University
• Self-Supervision & Contrastive Frameworks – A Vision-Based Review from Stanford University
• Picking on the Same Person – Does Algorithmic Monoculture Homogenize Outcomes? from Stanford University
• HAI Weekly Seminar with Jeff Ding – The Rise and Fall of Great Technologies and Powers from Stanford University
• Towards Understanding and Building Embodied Intelligence from Stanford University
• Joint Learning Over Visual and Geometric Data from Stanford University
• Neural Networks in R and the MNIST Data – Lecture 10.R.1 from Stanford University
• Stanford Seminar – Computing with Physical Systems 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 from Stanford University
• Interactive Imitation Learning: Planning Alongside Humans from Stanford University
• Improving Natural Language Understanding Through Adversarial Testing from Stanford University
• Stanford Seminar – Computer-Designed Organisms – Josh Bongard from Stanford University
• Current Status of TinyML and the Enormous Opportunities Ahead – Panel Discussion from Stanford University
• Stanford Seminar – New Chip Technologies for AI and for Asia – The Edge is Where the Action Is from Stanford University
• Stanford Lecture – Don Knuth – “Dancing Links” 2018 from Stanford University
• Petascale Deep Learning on a Single Chip from Stanford University
• Multiscale Dataflow Computing – Competitive Advantage at the Exascale Frontier from Stanford University
• Unethical Algorithms of Massive Scale from Stanford University
• Stanford Seminar – Autonomous Driving, Are We There Yet? – Technology, Business, Legal Considerations from Stanford University
• Deep Learning in the Age of Zen, Vega, and Beyond from Stanford University
• Stanford Seminar – The Time-Less Datacenter from Stanford University
• Technology for Today’s Problems – Addressing Global Challenges with Semiconductor Advancements from Stanford University
• Input with Deeply Custom Interfaces from Stanford University
• Democratizing Model Discovery with Neural Networks from Stanford University
• Retrieval Augmented Language Models – Stanford CS25 Lecture from Stanford University
• Towards Safe and Efficient Learning in the Physical World – Stanford Seminar from Stanford University
• Data Compression I – Lecture 4: Huffman Codes from Stanford University
• Stanford CS236: Deep Generative Models – Lecture 13 – Score Based Models from Stanford University
• Deep Generative Models – Lecture 2: Background from Stanford University
• Deep Generative Models – Energy Based Models – Lecture 12 from Stanford University
• Deep Generative Models – Normalizing Flows – Lecture 9 from Stanford University
• Deep Generative Models – Energy Based Models – Lecture 14 from Stanford University
• Deep Generative Models – Lecture 6: Variational Autoencoders from Stanford University
• Evaluation of Generative Models – Stanford CS236 Deep Generative Models Lecture 15 from Stanford University
• Stanford CS236: Deep Generative Models – Lecture 7 – Normalizing Flows from Stanford University
• Stanford CS236 – Deep Generative Models – Lecture 1: Introduction from Stanford University
• Aligning Open Language Models – Stanford CS25 Lecture from Stanford University
• HECTOR – Multimodal Deep Learning Model for Predicting Recurrence Risk in Endometrial Cancer from Stanford University
• Soma Design: Intertwining Aesthetics, Movement and Emotion in Design Work from Stanford University
• Using Pose and Action Estimation to Analyze Theater Performances from Stanford University
• Public AI Assistant to Worldwide Knowledge: 21st Century Knowledge Preservation and Access with AI from Stanford University
• Creating a Public AI Assistant to Worldwide Knowledge from Stanford University
• Exploring a Corpus – From Paper to Multilingual Chatbots from Stanford University
• Deploying AI in Organizations and Society from Stanford University
• Toward Relieving Clinician Burden by Automatically Generating Progress Notes from Stanford University
• Empire of AI – The Rise of OpenAI and the Future of Artificial Intelligence from Stanford University
• Shaping AI’s Impact on Billions of Lives from Stanford University
• Accelerating the AI Revolution in Medicine from Stanford University
• Using Physics Informed Neural Networks to Build Digital Twins of Cardiac Hemodynamics from Stanford University
• AI in Healthcare Series – Empowering Patients from Stanford University
• Charting the Future – Open Innovation in an Era of Global AI Competition from Stanford University
• Collaboration Between Clinicians and Vision-Language Models in Radiology Report Generation from Stanford University
• Tractable Novelty Exploration Over Continuous and Discrete Sequential Decision Problems from University of Melbourne
• MIT: Neural Rendering from Alexander Amini
• Learning Steering for Parallel Autonomy from Alexander Amini
• MIT: Image Domain Transfer – NVIDIA Guest Lecture from Alexander Amini
• Convolutional Neural Networks from Alexander Amini
• Automating the Search for Artificial Life with Foundation Models from Massachusetts Institute of Technology
• GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models from Massachusetts Institute of Technology
• Giving Sight to Speech Models from Massachusetts Institute of Technology
• Deep Subspace Learning for Dynamic MR Image Reconstruction from Yale University
• The Software Design Document – Requirements to Implementation – Medical Software Course 7.1 from Yale University
• The Ultimate Software Design Document Template Guide – Medical Software Course – 7.2 from Yale University
• Medical Software Life Cycles – Agile Development – 5.2 from Yale University
• Revolutionizing Patient Care – EHR and PACS – 3.4 from Yale University
• Introduction to Classical and Quantum Error Correction – Class 1 from Yale University
• Medium Earth Orbit and GPS – Rocket Science for Everyone – 2.2 from Yale University
• 人工智能原理 from Peking University
• 数据结构(上) from Tsinghua University
• 数据结构(下) from Tsinghua University
• 操作系统 from Tsinghua University
• 算法设计与分析 from Tsinghua University
• 区块链和加密数字货币 from Tsinghua University
• 大数据机器学习 from Tsinghua University
• 计算几何 from Tsinghua University
• 机器学习概论 from Tsinghua University
• Data Structures and Algorithm Design Part II from Tsinghua University
• 分布式数据系统应用实战-入门 from Tsinghua University
• Computational Geometry from Tsinghua University
• 信息素养——学术研究的必修课 from Tsinghua University
• 软件需求工程 from Tsinghua University
• 计算未来云讲坛（2020）——清华大学计算机系前沿系列讲座 from Tsinghua University
• AI and Sustainable Convergence from Tsinghua University
• Machine Learning for Personalized Healthcare – Opportunities, Challenges and Insights from New York University (NYU)
• Brooklyn Quant Experience: A Brief History of Quant Investing – From Traditional Equity Factors to Machine Learning from New York University (NYU)
• Machine Learning for Personalization from New York University (NYU)
• Curiosity Styles in the Natural and Artificial Wild from New York University (NYU)
• 软件设计模式 from University of Science and Technology of China
• Linux操作系统分析 from University of Science and Technology of China
• Information Theory: Defining Entropy and Information – Lecture 1 from University of Oxford
• Basic Properties of Information Theory – Lecture 2 from University of Oxford
• Introducing Codes in Information Theory – Lecture 3 from University of Oxford
• Information Theory: Typical Sequences and the Kraft-McMillan Inequality – Lecture 4 from University of Oxford
• Information Theory: Optimal Codes and Block Coding – Lecture 5 from University of Oxford
• Information Theory: Shannon’s Code – Lecture 6 from University of Oxford
• Information Theory: Tunstall’s Code – Lecture 8 from University of Oxford
• Kathleen Creel- Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems from Stanford University
• Highly Optimized Quantum Circuits Synthesized via Data-Flow Engines from Stanford University
• Stanford Seminar 2022 – Transformer Circuits, Induction Heads, In-Context Learning from Stanford University
• Stanford Seminar 2022 – DeepMind’s Perceiver and Perceiver IO: New Data Family Architecture from Stanford University
• Stanford Seminar – Mixture of Experts Paradigm and the Switch Transformer from Stanford University
• Stanford Seminar 2022 – Transformers in Vision: Tackling Problems in Computer Vision from Stanford University
• Stanford Seminar – Dataflow for Convergence of AI and HPC – GroqChip from Stanford University
• Stanford Seminar – Universal Intelligent Systems by 2030 – Carl Hewitt and John Perry from Stanford University
• Stanford Seminar – Centaur Technology’s Deep Learning Coprocessor from Stanford University
• Nanosecond-Level Clock Synchronization in a Data Center from Stanford University
• Extending the Theory of ML for Human-Centric Applications from Stanford University
• Autonomous Driving – AI’s Biggest Endeavor, James Peng of Pony.ai from Stanford University
• The Case for Learned Index Structures from Stanford University
• Tiny Functions for Codecs, Compilation, and – Maybe – Soon Everything from Stanford University
• Stanford Seminar – Computing with High-Dimensional Vectors from Stanford University
• Generalized Reversible Computing and the Unconventional Computing Landscape from Stanford University
• Stanford Seminar – Beyond Floating Point – Next Generation Computer Arithmetic from Stanford University
• The Analysis of Algorithms – 2015 Recreation of 1969 Lecture from Stanford University
• Designing the iPhone’s Magic Flute – Ge Wang from Stanford University
• Runway – A New Tool for Distributed Systems Design from Stanford University
• Stanford CS109 – Fairness in Probability for Computer Scientists – Lecture 26 from Stanford University
• How I Learned to Stop Worrying and Love the Transformer – Stanford CS25 Lecture from Stanford University
• Dancing Cells – Efficient Data Structures for Combinatorial Problem Solving from Stanford University
• Data Compression – Video Compression – Lecture 18 from Stanford University
• Data Compression I – Course Introduction and Lossless Compression Basics – Lecture 1 from Stanford University
• Data Compression I – Lecture 5: Asymptotic Equipartition Property from Stanford University
• Context-based Arithmetic Coding and LLM Compression – Stanford EE274 Lecture 9 from Stanford University
• Data Compression I – Lecture 3: Kraft Inequality, Entropy, and Introduction to SCL from Stanford University
• Stanford EE274: Data Compression – Beyond IID Distributions: Conditional Entropy – Lecture 8 from Stanford University
• LZ and Universal Compression – Lecture 10 from Stanford University
• Arithmetic Coding – Stanford EE274 Data Compression I Lecture 6 from Stanford University
• Learnt Image Compression – Lecture 16 from Stanford University
• Lossy Compression Basics and Quantization – Lecture 11 from Stanford University
• Asymmetric Numeral Systems (ANS) – Data Compression Lecture 7 from Stanford University
• Deep Generative Models – Autoregressive Models – Lecture 3 from Stanford University
• Deep Generative Models – Discrete Latent Variable Models – Lecture 17 from Stanford University
• Deep Generative Models – Lecture 5: Variational Autoencoders from Stanford University
• Deep Generative Models – Diffusion Models for Discrete Data – Lecture 18 from Stanford University
• Deep Generative Models – Lecture 10: GANs from Stanford University
• Deep Generative Models – Lecture 8: GANs from Stanford University
• Deep Generative Models – Energy Based Models – Lecture 11 from Stanford University
• Demystifying Mixtral of Experts – Stanford CS25 Lecture from Stanford University
• Transformers that Transform Well Enough to Support Near-Shallow Architectures – Stanford CS25 Lecture from Stanford University
• Shaping the Future of AI from the History of Transformer Architectures – Stanford CS25 from Stanford University
• Zero Knowledge Proofs for Verifiable Computation on Blockchain Systems – Panel Discussion from Stanford University
• Divide and Conquer – Concept-based Models for Efficient Transfer Learning from Stanford University
• Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering from Stanford University
• Analyzing and Exposing Vulnerabilities in Language Models – Session 131 from Stanford University
• Public AI Assistant to Worldwide Knowledge: 21st Century Knowledge Preservation and Access with AI from Stanford University
• Public AI Assistant to Worldwide Knowledge: Answering Queries of Structured and Unstructured Data from Stanford University
• AI in Healthcare Series: From Decision Support to Drug Prescriptions from Stanford University
• AI Audits and Autonomy in Sociotechnical Systems from Stanford University
• Transformers in Diffusion Models for Image Generation and Beyond from Stanford University
• The Future of Personalized Healthcare Technology with AI from Stanford University
• The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models from Stanford University
• Managed by AI – Artificial Pancreas and Contemporary Treatment of Diabetes from Stanford University
• LoRKD – Low-Rank Knowledge Decomposition for Medical Foundation Models from Stanford University
• Diffusion Models in Medical Imaging from Stanford University
• Adaptive Federated Knowledge Injection into Medical Foundation Models from Stanford University
• How Can We Diagnose and Treat Bias in LLMs for Clinical Decision-Making – MedAI #142 from Stanford University
• The Role of Business – Policy Implications of Industry Leadership in Artificial Intelligence from Stanford University
• AI in Healthcare Series – Leveraging GPT-5, Cosmos, and Predictive Models for Better Outcomes from Stanford University
• Opportunistic Screening for Pancreatic Cancer – Multimodal AI Fusion of CT Imaging and Radiology Reports from Stanford University
• Introduction to Deep Learning – Lecture 1 from Stanford University
• The New DBification of ML-AI from University of Melbourne
• Convolutions in Image Processing – MIT 18.S191 Fall 2020 – Week 1 from The Julia Programming Language
• Aligning Language Models with LESS Data and Simple Preference Optimization (SimPO) from Massachusetts Institute of Technology
• The Promises and Pitfalls of Open-source Agent Systems from Massachusetts Institute of Technology
• Probabilistic Methods in Combinatorics from Massachusetts Institute of Technology
• Redefining Context for Powerful Test-Time Adaptation Using Unlabeled Data from Massachusetts Institute of Technology
• Inference-Time Policy Customization Through Interactive Task Specification from Massachusetts Institute of Technology
• The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities from Massachusetts Institute of Technology
• Can Diffusion Model Disentangle? A Theoretical Perspective from Massachusetts Institute of Technology
• Recursive Reasoning with LLMs and VLMs from Massachusetts Institute of Technology
• Reducing Hallucinations in LLMs via Decoding, Detection, and Citation from Massachusetts Institute of Technology
• Quantifying Generalization Complexity for Large Language Models from Massachusetts Institute of Technology
• The Structures of Power in Early Modern England – Politics, Religion, and Society under the Tudors from Yale University
• Fast Motion-Resolved 4D MRI Using Convolutional Networks Without Data Consistency 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
• Mastering Your SRS Template for Medical Software Course from Yale University
• Navigating the Medical Software Life Cycle – A Complete Guide – 5.4 from Yale University
• Diving Into Medical Software – Your Course Introduction – 1.1 from Yale University
• Navigating AI/ML Challenges in Medical Software Development – 11.6 from Yale University
• Demystifying Deep Learning – Neural Networks and Medical AI Applications – 11.3 from Yale University
• Navigating AI/ML Regulations – Global Guidance for Medical Software Course – 11.4 from Yale University
• Demystifying AI and ML – A Core Introduction for Medical Software Course – 11.1 from Yale University
• Signal Detection and ROC Curves – Optimizing Medical Software Decisions – 9.5 from Yale University
• Measurement in Circuit Quantum Electrodynamics – Class 7 from Yale University
• Decoding Problem and Statistical-Mechanical Mapping – Class 6 from Yale University
• Erasure Errors and Rydberg Atoms – Class 5 from Yale University
• Quantum Codes, Stabilizers, and Computation – Class 2 from Yale University
• Hardware Efficient Encodings – Cat Qubits and Dual-Rail Qubits – Class 4 from Yale University
• Introduction to Bosonic Modes and Kraus Operators – Class 3 from Yale University
• 移动图形概论 from Peking University
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• 高级大数据系统 from Tsinghua University
• 软件理论基础 from Tsinghua University
• 高级操作系统 from Tsinghua University
• 操作系统(RISC-V) from Tsinghua University
• 计算机是如何实现智能的 from Tsinghua University
• 学堂在线×快手《新知如师说》清华名师系列讲座 from Tsinghua University
• 闲话AI：自然语言理解难在哪儿？ from Tsinghua University
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Programming (151)

• Computing in Python I: Fundamentals and Procedural Programming from Georgia Institute of Technology ★★★★★(272)
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• CS50’s Introduction to Programming with Python from Harvard University ★★★★★(11)
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• Introduction To MATLAB Programming (Fall 2011) from Massachusetts Institute of Technology ★★★★★(8)
• CS50’s Introduction to Programming with Scratch from Harvard University ★★★★☆(6)
• Creating Video Games from Massachusetts Institute of Technology ★★★★☆(6)
• Android App Development Winter 2019 from Stanford University ★★★★★(6)
• 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)
• CS50’s Introduction to Databases with SQL from Harvard University ★★★★★(4)
• Databases: Relational Databases and SQL from Stanford University ★★★★☆(3)
• Cloud Computing – What’s on the Horizon with Dr. Timothy Chou from Stanford University ★★★★☆(3)
• CS50’s Mobile App Development with React Native from Harvard University ★★★★★(2)
• Database Systems Concepts & Design from Georgia Institute of Technology ★★★★☆(1)
• Automated Software Testing: Model and State-based Testing from Delft University of Technology ★★★★★(1)
• Using JavaScript, JQuery, and JSON in Django from University of Michigan ★★★★★(1)
• Databases: Semistructured Data from Stanford University ★★★★★(1)
• Programación para todos (empezando con Python) from University of Michigan ★★★★☆(1)
• Seven Databases in Seven Weeks – Fall 2014 from Carnegie Mellon University ★★★★★(1)
• Advanced Database Systems – Spring 2019 from Carnegie Mellon University ★★★★★(1)
• Stanford Seminar – Optional Static Typing for Python from Stanford University ★★★★★(1)
• Advanced Database Systems – Spring 2017 from Carnegie Mellon University ★★★★☆(1)
• Rocket- Securing the Web at Compile-Time from Stanford University ★★★★★(1)
• Stanford Seminar – KUtrace from Stanford University ★★★★★(1)
• Stop User Errors – Mastering Usability Engineering in Medical Software Course from Yale University ★★★☆☆(1)
• Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability from Delft University of Technology
• Quantitative Methods for Biology from Harvard University
• CS 6340: Software Analysis & Testing from Georgia Institute of Technology
• Databases: Advanced Topics in SQL from Stanford University
• Database Design and Basic SQL in PostgreSQL from University of Michigan
• R Programming Fundamentals from Stanford University
• CS50’s Introduction to Programming with R from Harvard University
• Creating Virtual Reality (VR) Apps from University of California, San Diego
• How Virtual Reality Works from University of California, San Diego
• Introduction to Object-Oriented Programming with Java III: Exceptions, Data Structures, Recursion, and GUIs from Georgia Institute of Technology
• Databases: Modeling and Theory from Stanford University
• Using JavaScript and JSON in Django from University of Michigan
• Mobile Application Experiences from Massachusetts Institute of Technology
• UML Class Diagrams for Software Engineering from KU Leuven University
• Worldbuilding for Video Games from The University of British Columbia
• Real-Time Audio Signal Processing in Faust from Stanford University
• JSON and Natural Language Processing in PostgreSQL from University of Michigan
• Global Software Development from Delft University of Technology
• Statistical Learning with Python from Stanford University
• Databases: OLAP and Recursion from Stanford University
• Introduction to Object-Oriented Programming with Java II: Object-Oriented Programming and Algorithms from Georgia Institute of Technology
• Introduction to Object-Oriented Programming with Java I: Foundations and Syntax Basics from Georgia Institute of Technology
• Intermediate PostgreSQL from University of Michigan
• Minecraft, Coding and Teaching from University of California, San Diego
• Programming Reactive Systems from École Polytechnique Fédérale de Lausanne
• Building Web Applications in Django from University of Michigan
• Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan
• Web Application Technologies and Django from University of Michigan
• Introduction à la science des données sociales avec R from Université de Montréal
• Django Features and Libraries from University of Michigan
• MATLAB et Octave pour débutants from École Polytechnique Fédérale de Lausanne
• Estructuras de Datos con Python from University of Michigan
• C++ Programming | C++程序设计 from Peking University
• Java程序设计 | Java Programming from Peking University
• CS193p – Developing Apps for iOS from Stanford University
• Virtual Reality from University of Illinois at Urbana-Champaign
• How to Design Addictive Games from Stanford University
• iPhone Application Development Spring 2020 from Stanford University
• 程序设计基础 from Peking University
• Stanford Seminar – Programming Should Be More Than Coding from Stanford University
• Stanford Seminar – Stories from CoCoLab: Probabilistic Programs, Cognitive Modeling, & Smart Web Pages from Stanford University
• Ciencia de Datos: Fundamentos de R from Harvard University
• Extended Reality for Everybody – Michael Nebeling from Stanford University
• CS 241: System Programming from University of Illinois at Urbana-Champaign
• C++语言程序设计基础 from Tsinghua University
• Stanford Seminar – Accessible Virtual Reality for People with Limited Mobility from Stanford University
• Haskell: Lecture notes and assignments from University of Pennsylvania
• Learn Computer Science Online from University of Illinois at Urbana-Champaign
• Computer Language Engineering (SMA 5502) from Massachusetts Institute of Technology
• Advanced Database Systems – Spring 2016 from Carnegie Mellon University
• User Experience (UX) Design: Human Factors and Culture in Design from Tsinghua University
• Intro to Database Systems – Fall 2017 from Carnegie Mellon University
• Stanford Seminar – Mind Your State for Your State of Mind from Stanford University
• Making Teamwork an Objective Discipline – Sid Sijbrandij CEO & Chairman of GitLab from Stanford University
• Stanford Seminar – Concatenative Programming- From Ivory to Metal from Stanford University
• Programming Languages – The Fundamental Tools of the Computer Age from University of Melbourne
• Intro to Database Systems – Fall 2018 from Carnegie Mellon University
• Advanced Database Systems – Spring 2018 from Carnegie Mellon University
• Paul’s Disciples – Pseudepigraphic Letters to Colossians and Ephesians from Yale University
• LaTeX course from University of Amsterdam
• Intro to Database Systems – Fall 2021 from Carnegie Mellon University
• Advanced Database Systems – Spring 2020 from Carnegie Mellon University
• Hardware Accelerated Database Lectures – Fall 2018 from Carnegie Mellon University
• 程序设计基础 from Tsinghua University
• 学做小程序——基础篇 from Tsinghua University
• 计算机程序设计基础 from Tsinghua University
• Stanford Seminar – The Future of Edge Computing from an International Perspective from Stanford University
• Missing Semester IAP 2020 from Massachusetts Institute of Technology
• Structure and Interpretation of Computer Programs from Massachusetts Institute of Technology
• Internet Technology in Local and Global Communities from Massachusetts Institute of Technology
• Intro to Database Systems – Fall 2019 from Carnegie Mellon University
• Time Series Database Lectures from Carnegie Mellon University
• The Databaseology Lectures – Fall 2015 from Carnegie Mellon University
• PACS & AI – From Integration to Cloud from Yale University
• Java程序设计 from Tsinghua University
• Designing the Interactive Paper from Stanford University
• Stanford Seminar – PyWren – Pushing Microservices to Teraflops from Stanford University
• Living in Information Everywhere from Stanford University
• Wearing a VR Headset While Driving to Improve Vehicle Safety from Stanford University
• Master Code Management – Essential Revision Control Systems for Medical Software Course from Yale University
• Software Testing Essentials – Finding Bugs and Ensuring Quality in Medical Software – 8.3 from Yale University
• C++语言程序设计进阶 from Tsinghua University
• 学做小程序——实战篇：树洞小程序 from Tsinghua University
• 学做小程序–云开发篇：近义词小程序 from Tsinghua University
• Jeremy Bailenson: Your Mind on the Metaverse from Stanford University
• Stanford Seminar – TechFail: From Intersectional Inaccessibility to Inclusive Design from Stanford University
• Understanding the Utility of Haptic Feedback in Telerobotic Devices from Stanford University
• Stanford Seminar – I Forgot, I Invented Hypertext – Ted Nelson from Stanford University
• Edge Computing and the Evolution of AR-VR – Panel Discussion from Stanford University
• Stanford Seminar – Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems from Stanford University
• Graph Analysis of Russian Twitter Trolls Using Neo4j from Stanford University
• An Alternative to the American Way of Innovation from Stanford University
• Integrating Interactive Devices with the User’s Body from Stanford University
• Beyond Benchmarks – Building a Science of AI Measurement from Stanford University
• VR/AR in IR – Mixed Reality in Medicine from Yale University
• JAVA程序设计进阶 from Tsinghua University
• Web前端攻城狮 from Tsinghua University
• 基于R语言的社会统计分析 from Tsinghua University
• Making the Invisible Visible – Observing Complex Software Dynamics from Stanford University
• Stanford Seminar – Robosion: Software Platform for Lifelike Humanoids from Stanford University
• From Flat to Phantasmal: Enhancing User Experiences Through Spatial Computing Advancements from Stanford University
• Essential Medical Software Verification and Testing Strategies – 8.5 from Yale University
• VC++面向对象与可视化程序设计（上）：Windows编程基础 from Tsinghua University
• VC++面向对象与可视化程序设计（下）：MFC编程基础 from Tsinghua University
• 汇编语言程序设计 from Tsinghua University
• 基于Linux的C++ from Tsinghua University
• JAVA程序设计进阶 from Tsinghua University
• 面向对象程序设计（C++） from Tsinghua University
• 游戏程序设计 from Tsinghua University
• C++编程训练营 from Tsinghua University
• 低时延网络挑战赛：从5G和边缘计算到互联网 from Tsinghua University
• 界面设计导论 from Tsinghua University
• 高级数据库系统 from Tsinghua University
• 游戏分析与评测 from Tsinghua University

Data Science (110)

• Fundamentals of Qualitative Research Methods from Yale University ★★★★★(187)
• The Analytics Edge from Massachusetts Institute of Technology ★★★★★(80)
• Next in Data Visualization – Michelle Borkin – Radcliffe Institute from Harvard University ★★★★☆(61)
• Python for Data Science from University of California, San Diego ★★★★☆(48)
• 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)
• Mining Massive Datasets from Stanford University ★★★★★(24)
• Statistics and R from Harvard University ★★★★☆(20)
• DCO042 – Python For Informatics from University of Michigan ★★★★★(14)
• Data Science: R Basics from Harvard University ★★★★★(12)
• Foundations of Data Analysis – Part 1: Statistics Using R from The University of Texas at Austin ★★★★☆(8)
• Algorithms for Big Data from Harvard University ★★★★★(8)
• Data Analysis Essentials from Imperial College London ★☆☆☆☆(7)
• Data Analytics at the Exascale for Free Electron Lasers Project from Stanford University ★★★★★(7)
• Data Science: Visualization from Harvard University ★★★★★(6)
• Computing for Data Analysis from Georgia Institute of Technology ★★★☆☆(6)
• Introduction to Analytics Modeling from Georgia Institute of Technology ★★★★☆(5)
• STAT115 2020 from Harvard University ★★★★★(4)
• The Analytics Edge (Spring 2017) from Massachusetts Institute of Technology ★★★★★(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)
• Data Science: Building Machine Learning Models from Harvard University ★★★★☆(3)
• High-Dimensional Data Analysis from Harvard University ★★★★☆(3)
• Introduction to Data Science with Python from Harvard University ★★★★☆(3)
• 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)
• AI Skills for Engineers: Data Engineering and Data Pipelines from Delft University of Technology ★★★★☆(2)
• Stanford Seminar – When DNA Meets AI 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)
• Data Science: Productivity Tools from Harvard University ★★★★★(1)
• Data Science: Inference and Modeling from Harvard University ★★★★☆(1)
• Policy Analysis Using Interrupted Time Series from The University of British Columbia ★★★★★(1)
• STAT115 2018 from Harvard University ★★★★★(1)
• Big Data Analytics from California Institute of Technology ★★★☆☆(1)
• MIT Deep Learning in Life Sciences Spring 2020 from Massachusetts Institute of Technology ★★★★★(1)
• MIT CompBio Course Projects Fall 2019 from Massachusetts Institute of Technology ★★★★☆(1)
• Soccermatics – Could a Premier League Team One Day Be Managed by a Mathematician? from University of Oxford ★★★★★(1)
• Data and Visual Analytics from Georgia Institute of Technology
• Principles, Statistical and Computational Tools for Reproducible Data Science from Harvard University
• Data Science: Capstone from Harvard University
• CSE 8803 Special Topics: Big Data from Georgia Institute of Technology
• Introduction to Genomic Data Science from University of California, San Diego
• Foundations of Data Analytics from The Hong Kong University of Science and Technology
• Statistical Computing with R – a gentle introduction from University College London
• Data Analytics for Business from Georgia Institute of Technology
• Dynamic Programming: Applications In Machine Learning and Genomics from University of California, San Diego
• Algorithms and Data Structures Capstone from University of California, San Diego
• Graph Algorithms in Genome Sequencing from University of California, San Diego
• Big Data for Reliability and Security from Purdue University
• Data Creation and Collection for Artificial Intelligence via Crowdsourcing from Delft University of Technology
• Big Data Computing with Spark from The Hong Kong University of Science and Technology
• Advanced Big Data Systems | 高级大数据系统 from Tsinghua University
• Data Mining and Knowledge Discovery from The Hong Kong University of Science and Technology
• Mathematical Methods for Data Analysis 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
• 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
• Fondamentaux de la science des données from Université de Montréal
• Learning Time Series with Interventions from Massachusetts Institute of Technology
• Knowledge Inference and Structure Discovery for Education from University of Pennsylvania
• STAT115 from Harvard University
• Science des données et santé from Université de Montréal
• The impact of big data on healthcare from University of Cambridge
• Stanford Seminar – Theories of Inference for Visual Analysis from Stanford University
• Mining Online Data Across Social Networks from Stanford University
• Introduction to R and Geographic Information Systems (GIS) from Massachusetts Institute of Technology
• How to Analyze Research Data – Kristin Sainani from Stanford University
• Next in Data Visualization – Interactive Systems for Intelligence Augmentation from Harvard University
• Data Mining – The Tool of the Information Age from Stanford 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 – Anirudh Todi of Twitter from Stanford University
• Mathematics of Big Data and Machine Learning from Massachusetts Institute of Technology
• Data Analysis for Social Scientists from Massachusetts Institute of Technology
• Data Analysis for Social Scientists from Massachusetts Institute of Technology
• Stanford Seminar – Developing Design Spaces for Visualization – Tamara Munzner from Stanford University
• Stanford Seminar – Forecasting and Predicting the Future from Stanford University
• The End of Privacy from Stanford University
• Análisis de datos: Llévalo al MAX() from Delft University of Technology
• Jupyter Notebooks and Academic Publication from Stanford University
• Quantitative Biology Workshop from Massachusetts Institute of Technology
• Method of Detection – The Critical Missing Link in U.S. Cancer Registries from Yale University
• Stanford Seminar – Algorithmic Extremism- Examining YouTube’s Rabbit Hole of Radicalization from Stanford University
• AI for Academic Search （人工智能助力学术搜索） from Tsinghua University
• Big Data’s Big Deal – Viktor Mayer-Schonberger from University of Oxford
• Stanford Seminar – Mobilizing the Future from Stanford University
• Data for the People – Andreas Weigend of Social Data Lab from Stanford University
• Big Data, Big Impact – Preventing Drug Interactions, Understanding Behavior, and Climate Change from Stanford University
• Understanding the World Through Data from Massachusetts Institute of Technology
• 大数据技术与应用 from Tsinghua University
• Communicating Complex Statistical Ideas to the Public – Lessons from the Pandemic – D. Spiegelhalter from University of Oxford
• Grid Modernization and the Integration of Distributed Resources from Stanford University
• Stanford Seminar – Zhang Lin on Mobile Urban Sensing in Beijing from Stanford University
• Human-Machine Symbiosis in Data Visualization from Stanford University
• Harnessing Data for Social Impact – Empowering Communities through Visualization and Social Computing from Stanford University
• 大数据系统基础 from Tsinghua University
• 疫情防控，大数据分析大显身手 from Tsinghua University
• 清华大数据应用实践：快速搭建基于数据的应用 from Tsinghua University
• 数据与智能技术应用 from Tsinghua University
• 基于数据驱动的网络技术应用 from Tsinghua University
• 提升检索（Search）力就是提升研究（Research）力 from Tsinghua University
• 清华大学计算机科学与技术系六十周年系庆学术报告（四）大数据 from Tsinghua University
• “清华数为”大数据智能软件栈 from Tsinghua University
• 大数据可视化 from Zhejiang University

Information Security (InfoSec) (55)

• Web Security from Stanford University ★★★★★(87)
• Computer Systems Security from Massachusetts Institute of Technology ★★★★★(52)
• Web Security Fundamentals from KU Leuven University ★★★★☆(22)
• Engineering Cyber Resiliency – A Pragmatic Approach from Stanford University ★★★★★(22)
• Hash, Hack, Code – Emerging Trends in Cyber Security from Stanford University ★★★★★(12)
• CS50’s Introduction to Cybersecurity from Harvard University ★★★★★(6)
• Intro to Information Security from Georgia Institute of Technology ★★☆☆☆(2)
• Network Security from Georgia Institute of Technology ★★★★★(1)
• Cyber Security Economics from Delft University of Technology ★★☆☆☆(1)
• Stanford Seminar – The Current State of Cybersecurity from Stanford University ★★★★★(1)
• Big Breaches – What We Learned From the World’s Most Disruptive Cybersecurity Attacks from Stanford University ★★★★☆(1)
• Solving Cybersecurity as an Economic Problem from Stanford University ★★★★☆(1)
• Stanford Seminar – Computer Security- The Mess We’re In, How We Got Here, and What to Do About It from Stanford University ★★★★★(1)
• Stanford Seminar – Preventing Successful Cyberattacks Using Strongly-Typed Actors from Stanford University ★★★★☆(1)
• Cyber-Physical Systems Security from Georgia Institute of Technology
• Mobile Payment Security from New York University (NYU)
• La cybersécurité en milieu universitaire from Université de Montréal
• Building Your Shield – Mapping the Cybersecurity Market from Stanford University
• 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
• Cybersecurity for Critical Urban Infrastructure from Massachusetts Institute of Technology
• Security Challenges in 5G Wireless and Beyond from New York University (NYU)
• Cybersecurity in Corporate Law: Key Issues for Practitioners and Policy-makers from New York University (NYU)
• Anatomy of an Attack – Understanding and Protecting Against Cyber Threats from New York University (NYU)
• Stanford Seminar – Online Political Ad Transparency from Stanford University
• Taking Memory Forensics to the Next Level from New York University (NYU)
• Tales from the Risks Forum from Stanford University
• Cybersecurity Career Insights – From Tandon Cyber Fellow to VP at U.S. Bank from New York University (NYU)
• Cyber Security: Building Compromise Resilient Systems and Key Management – Dr. Justin Cappos from New York University (NYU)
• Too Big to Fail: Cybersecurity Issues in Financial Services from New York University (NYU)
• angr: Binary Analysis Framework – Demonstration and Analysis from New York University (NYU)
• Stanford Webinar – To Attribute or Not Attribute, Is That the Question? from Stanford University
• Industry Insights: Cyber Security – Trends, Impacts, and Career Opportunities from University of Melbourne
• Cybersecurity: Keeping the Lights On – Practical Challenges in Utilities – Keynote from New York University (NYU)
• Locking the Web Open – A Call for a New, Decentralized Web 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
• How Can Privacy Exist in a Data-Driven World? – Stanford Seminar from Stanford University
• Privacy and the Power of Unknowing from Stanford University
• Hacking Reality: Technology for Detecting Deep Fakes from New York University (NYU)
• Firing Rounds at the Analysis Shooting Gallery – CSAW’16 Security Workshop from New York University (NYU)
• Lessons from Mirai and the Current State of IoT Security from Stanford University
• Exploiting Modern Microarchitectures – Meltdown, Spectre, & Other Hardware Attacks from Stanford University
• RowHammer, RowPress and Beyond: Can We Be Free of Bitflips Soon? – Stanford Seminar from Stanford University
• Proving Confidentiality and Its Preservation for Mixed-Sensitivity Concurrent Programs from University of Melbourne
• Protect Your Patients – Cybersecurity Essentials for Medical Software Course from Yale University
• Medical Software’s Lifespan – Maintenance and Safe Retirement Strategy from Yale University
• 网络安全博弈之路 from Tsinghua University
• Trading Privacy for Convenience in the Age of Technology – Part 2 from New York University (NYU)
• IC Layout Security – Hardening Integrated Circuit Designs Against Adversaries from New York University (NYU)
• Empirical Measurement of Security and Privacy in Technology Systems – Conversations with Dr. Damon McCoy from New York University (NYU)
• Dispelling the Top 10 Myths in Cybersecurity – AIG Cybersecurity Lecture from New York University (NYU)
• Democracy Confronts Cyber Insecurity – Lecture 9 from New York University (NYU)
• Web安全实践 from University of Science and Technology of China
• 高级计算机网络 from University of Science and Technology of China

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