CS Video Courses: The Complete Guide to the Largest Collection of Free Computer Science Video Lectures
What if you could attend lectures at MIT, Stanford, Berkeley, CMU, Harvard, Caltech, and dozens of other world-class universities — all for free, organized by topic, and accessible from anywhere? That's exactly what CS Video Courses delivers.
Created by Developer-Y in October 2016 and maintained by 111 contributors, this repository is the largest curated collection of Computer Science courses with video lectures on GitHub. With 76,900+ stars and 10,400+ forks, it dwarfs every other CS learning resource on the platform.
This isn't a collection of YouTube tutorials or short MOOCs — it's a carefully curated list of actual university-level courses with complete lecture series from the world's top institutions.
Key Stats
| Metric | Value |
|---|---|
| GitHub Stars | 76,900+ |
| Forks | 10,400+ |
| Contributors | 111 |
| Created | October 2016 |
| Author | Developer-Y |
| Topics | 30+ CS disciplines |
| Universities | MIT, Stanford, Berkeley, CMU, Harvard, Caltech, IITs, and many more |
| Active Since | 8+ years, continuously updated |
What Makes It Different
University-Level Only
The repository explicitly states: "Please create PR for actual college/University level courses. Please do not add links for small MOOCs, basic tutorials, or advertisements for some sites/channels." This strict curation policy is what separates it from generic awesome lists.
Complete Lecture Series
Each entry is a full course with a complete video lecture series — not a single talk or tutorial. Most entries include links to course websites, assignments, and supplementary materials.
Breadth and Depth
With 30+ topic categories, this covers virtually every subdiscipline of Computer Science, from introductory programming to quantum computing. Many categories contain dozens of courses at different levels.
Actively Maintained
Despite being created in 2016, the repository continues to receive updates. Recent additions include Spring 2025 courses like Berkeley's CS 188 and CMU's database systems courses through 2025.
The Complete Course Catalog
1. Introduction to Computer Science
The foundation. Courses from the most famous CS programs:
- Harvard CS50 — The world's most popular intro CS course (David J. Malan)
- MIT 6.0001 — Introduction to CS and Programming in Python
- MIT 6.001 — Structure and Interpretation of Computer Programs (SICP)
- Stanford CS106A/B/X — Programming Methodology/Abstractions in Java and C++
- UC Berkeley CS 61A — Structure and Interpretation of Computer Programs (Python)
- Stanford CS107 — Programming Paradigms
- Plus courses from UBC, Gatech, IIT Kanpur, TU Munich, and more
2. Data Structures and Algorithms
Massive section with courses spanning basic to advanced:
- MIT 6.006 — Introduction to Algorithms
- MIT 6.046 — Design and Analysis of Algorithms
- Stanford CS161 — Design and Analysis of Algorithms
- UC Berkeley CS 61B — Data Structures
- Princeton COS 226 — Algorithms (Sedgewick)
- Competitive programming, advanced algorithms, and complexity theory courses
3. Systems Programming
Three sub-categories covering the systems stack:
- Operating Systems: MIT 6.828, Berkeley CS162, and more
- Distributed Systems: MIT 6.824 (the gold standard), Martin Kleppmann's course
- Real-Time Systems: Specialized courses from IITs
4. Database Systems
From relational fundamentals to advanced distributed data:
- CMU 15-445 — Intro to Database Systems (Andy Pavlo) — with courses from 2017 through 2025
- CMU 15-721 — Advanced Database Systems
- Stanford DBclass — Database MOOC
- Caltech CS121/122 — Relational Database Systems
- NoSQL, in-memory databases, distributed data management
5. Software Engineering
Five sub-categories:
- Object-Oriented Design: MIT, IIT
- Software Engineering: UC Berkeley CS169, MIT, ETH Zurich
- Software Architecture: Various universities
- Concurrency: Parallel computing courses
- Mobile Application Development: Harvard CS76, Stanford CS193p
6. Artificial Intelligence
Classic AI courses from the world's best programs:
- Harvard CS50 AI — Introduction to AI with Python
- MIT 6.034 — Artificial Intelligence
- Stanford CS221 — AI: Principles and Techniques
- UC Berkeley CS 188 — Intro to AI (updated through Spring 2025)
- CMU 10-202 — Introduction to Modern AI
- Knowledge representation, semantic web, deductive databases
7. Machine Learning (Massive Section)
The largest category, with 15+ sub-sections spanning dozens of courses:
| Sub-Topic | Notable Courses |
|---|---|
| Intro to ML | Stanford CS229, Caltech CS156, Andrew Ng (Coursera) |
| Data Mining | Stanford, UIUC, Utah |
| Probabilistic Graphical Models | Stanford CS228, CMU 10-708 |
| Deep Learning | Stanford CS230, MIT 6.S191, NYU (Yann LeCun) |
| Reinforcement Learning | DeepMind x UCL, Stanford CS234, Berkeley CS285 |
| Advanced ML | Oxford, Stanford CS229M, CMU 10-702 |
| NLP | Stanford CS224N, CMU CS/LTI, Oxford |
| Generative AI & LLMs | Stanford CS25, Michigan, DeepLearning.ai |
| Computer Vision | Stanford CS231N, MIT 6.869, UMich EECS 498 |
| Time Series | TUM, IIT |
| Optimization | Stanford, MIT, Boyd's Convex Optimization |
| Unsupervised Learning | Berkeley, Stanford |
8. Computer Networks
- Stanford CS144 — Introduction to Computer Networking
- MIT 6.829 — Computer Networks
- Network protocols, wireless, content distribution
9. Math for Computer Scientists
Extensive section covering:
- MIT 6.042 — Mathematics for Computer Science
- MIT 18.06 — Linear Algebra (Gilbert Strang)
- MIT 18.065 — Matrix Methods in Data Analysis
- Discrete math, combinatorics, probability, statistics, calculus, optimization
- Courses from Princeton, Stanford, NPTEL, and more
10. Theoretical CS and Programming Languages
- MIT 6.045 — Automata, Computability, and Complexity
- Compilers (Stanford, Berkeley, NPTEL)
- Functional programming, type theory, formal verification
11. Computer Organization and Architecture
- MIT 6.004 — Computation Structures
- CMU 18-447 — Computer Architecture
- RISC-V, pipelining, cache hierarchies, digital logic
12. Security
- MIT 6.858 — Computer Systems Security
- Cryptography, network security, reverse engineering
- Applied cybersecurity courses from Stanford, IITs
13. Specialized Topics
| Topic | Example Courses |
|---|---|
| Embedded Systems | MIT, TUM, IIT |
| Computer Graphics | MIT 6.837, UC Davis |
| Image Processing & Computer Vision | UCF, IIT |
| Computational Physics | Caltech, Stanford |
| Computational Biology | MIT 6.047, Stanford GENE 214 |
| Quantum Computing | CMU, Caltech, MIT |
| Robotics & Control | Stanford, MIT, CMU |
| Computational Finance | MIT 18.S096, IIT |
| Network Science | Various |
| Blockchain Development | Berkeley, Stanford |
Universities Represented
The repository aggregates courses from an extraordinary breadth of institutions:
| Tier | Universities |
|---|---|
| US Elite | MIT, Stanford, UC Berkeley, CMU, Harvard, Caltech, Princeton |
| US Strong | University of Washington, Purdue, Cornell, University of Utah, Gatech |
| European | ETH Zurich, Oxford, Cambridge, TU Munich, HPI Potsdam, Edinburgh, FAU |
| Indian | IIT Kharagpur, IIT Madras, IIT Kanpur, NPTEL consortium |
| Others | University of Waterloo (Canada), ANU (Australia), Hebrew University, Monash |
CS Video Courses vs Alternative Resources
| Feature | CS Video Courses | Teach Yourself CS | OSSU | ForrestKnight | Class Central |
|---|---|---|---|---|---|
| Stars | 76.9K | N/A (site) | 180K+ | 20K+ | N/A (site) |
| Courses | Hundreds | ~14 recommended | ~40 | ~20 | 1000+ |
| Video-only | ✅ All have video | Some | Some | Some | Mixed |
| University-level | ✅ Strictly | ✅ | ✅ | Mostly | Mixed |
| Topics | 30+ | 9 core | ~15 | ~10 | All |
| Updated 2025 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Opinionated | ❌ Lists all | ✅ Picks best | ✅ Ordered path | ✅ Ordered | ❌ Lists all |
| Structure | By topic | By topic | By semester | By semester | By topic |
| Focus | Breadth + Depth | Depth (9 topics) | Full degree | Full degree | Discovery |
When to Choose Each
- CS Video Courses: When you want the widest possible selection of university-level video courses across every CS discipline. The "shopping catalog" approach — browse, compare, and pick courses that match your level and interest.
- Teach Yourself CS: When you want opinionated recommendations — the single best course for each of 9 core CS topics. Quality over quantity.
- OSSU: When you want a structured degree equivalent with a prescribed order and prerequisite management.
- ForrestKnight/open-source-cs: When you want a lightweight, focused degree path with fewer choices.
- Class Central: When you want a discovery platform with ratings, reviews, and filtering across all online learning platforms.
Use Cases
🎓 Self-Taught Computer Science Degree
Combine courses across all 30+ topics to build a comprehensive CS education equivalent to a university degree — entirely free.
📚 Topic Deep-Dive
Already know the basics? Jump directly to advanced sections like quantum computing, computational biology, or reinforcement learning with multiple course options at each level.
🏢 Team Learning
Engineering managers can create custom learning paths by selecting specific courses for team upskilling — databases, distributed systems, security, or ML.
🔬 Research Preparation
Graduate-level courses from CMU, Stanford, and MIT provide the theoretical depth needed for research. The must-read ML section alone spans 50+ courses.
🌍 Non-English Learners
Courses from IITs (India), TU Munich (Germany), and other international institutions provide options in different teaching styles and contexts.
Frequently Asked Questions
How many courses are listed?
The exact count changes as the list is updated, but there are hundreds of complete university-level courses with video lectures.
Are all videos free?
The vast majority are free. Some may be hosted on platforms that require free registration (like MIT OCW, NPTEL, or Coursera audit mode).
Is there a recommended order?
No. Unlike structured curricula (OSSU, Teach Yourself CS), this is a reference catalog organized by topic. You choose your own path.
How often is it updated?
Regularly. It includes courses as recent as Spring 2025 (e.g., Berkeley CS 188, CMU 15-445).
Can I contribute?
Yes. Pull requests are welcome for university-level courses. Small MOOCs, basic tutorials, and channel advertisements are explicitly rejected.
Conclusion
CS Video Courses is, by the numbers, the most popular curated CS learning resource on GitHub — and for good reason. In a single repository, you get access to hundreds of complete university-level video lecture series from the world's top institutions, organized across 30+ Computer Science disciplines.
With 76,900+ stars, 10,400+ forks, and 111 contributors maintaining the list over 8+ years, this is not just a collection — it's a living encyclopedia of Computer Science education. Whether you're a self-taught developer building foundational knowledge, a student supplementing your coursework, or an experienced engineer exploring a new domain, this repository is your definitive starting point.