|
1 | | -# OpenUnivCourses |
2 | | -FREE university courses in ML from Top Universities in CS |
3 | | - |
4 | | -* **Massachusetts Institute of Technology** |
5 | | - MIT6.S191: Introduction to Deep Learning |
6 | | - [2024](http://introtodeeplearning.com/) |
7 | | - [2023](http://introtodeeplearning.com/2023/index.html) |
8 | | - [2022](http://introtodeeplearning.com/2022/index.html) |
9 | | - [2021](http://introtodeeplearning.com/2021/index.html) |
10 | | - [2020](http://introtodeeplearning.com/2020/index.html) |
11 | | - MIT6.036: Introduction to Machine Learning |
12 | | - [2020](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-036-introduction-to-machine-learning-fall-2020/) |
13 | | - MIT6.S897: Machine Learning for Healthcare |
14 | | - [2019](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/) |
15 | | - MIT9.520: Statistical Learning Theory and Applications [YouTube](https://www.youtube.com/playlist?list=PLyGKBDfnk-iB4Xz_EAJNEgGF5I-6OzRNI) |
16 | | - [2019](http://www.mit.edu/~9.520/fall19/) |
17 | | - [2018](http://www.mit.edu/~9.520/fall18/) |
18 | | - [2017](http://www.mit.edu/~9.520/fall17/) |
19 | | - [2016](http://www.mit.edu/~9.520/fall16/) |
20 | | - [2015](http://www.mit.edu/~9.520/fall15/) |
21 | | - MIT Deep Learning and Artificial Intelligence Lectures by Lex Fridman |
22 | | - [2020](https://deeplearning.mit.edu/) |
23 | | - [2019](https://deeplearning.mit.edu/) |
24 | | - |
25 | | -* **Stanford University** |
26 | | - CS236: Deep Generative Models [2023](https://deepgenerativemodels.github.io/) |
27 | | - CS234: Reinforcement Learning [2024](http://web.stanford.edu/class/cs234/index.html) |
28 | | - CS231n: Convolutional Neural Networks for Visual Recognition [2024](http://cs231n.stanford.edu/) |
29 | | - CS230: Deep Learning [2023](https://cs230.stanford.edu/) |
30 | | - CS229: Machine Learning by Andrew Ng |
31 | | - [2024](http://cs229.stanford.edu/) |
32 | | - [2023D](https://cs229.stanford.edu/index.html-backup-fall23) |
33 | | - [2023C](https://cs229.stanford.edu/index.html-backup-summer23) |
34 | | - [2023B](https://cs229.stanford.edu/2023_index.html) |
35 | | - [2022D](https://cs229.stanford.edu/syllabus-fall2022.html) |
36 | | - [2022C](https://cs229.stanford.edu/index-sum22.html) |
37 | | - [2022B](https://cs229.stanford.edu/syllabus-spring2022.html) |
38 | | - [2021D](https://cs229.stanford.edu/syllabus-fall2021.html) |
39 | | - [2021B](https://cs229.stanford.edu/syllabus-spring2021.html) |
40 | | - [2020D](https://cs229.stanford.edu/syllabus-fall2020.html) |
41 | | - CS228: Probabilistic Graphical Models |
42 | | - [2024](https://cs228.stanford.edu/) |
43 | | - CS224n: Natural Language Processing with Deep Learning |
44 | | - [2024](http://web.stanford.edu/class/cs224n/) |
45 | | - CS221: Artificial Intelligence. Principles and Techniques |
46 | | - [2023D](https://stanford-cs221.github.io/autumn2023/) |
47 | | - [2023C](https://stanford-cs221.github.io/summer2023/) |
48 | | - [2023B](https://stanford-cs221.github.io/spring2023/) |
49 | | - [2022D](https://stanford-cs221.github.io/autumn2022/) |
50 | | - [2022B](https://stanford-cs221.github.io/spring2022/) |
51 | | - [2021D](https://stanford-cs221.github.io/autumn2021/) |
52 | | - [2021B](https://stanford-cs221.github.io/spring2021/) |
53 | | - [2021A](https://stanford-cs221.github.io/winter2021/) |
54 | | - [2020D](https://stanford-cs221.github.io/autumn2020/) |
55 | | - |
56 | | -* **Berkley University** |
57 | | - Full Stack Deep Learning |
58 | | - [2022](https://fullstackdeeplearning.com/course/2022/) |
59 | | - [2021](https://fullstackdeeplearning.com/spring2021/) |
60 | | - CS294: Deep Unsupervised Learning |
61 | | - [2024](https://sites.google.com/view/berkeley-cs294-158-sp24/home) |
62 | | - [2020](https://sites.google.com/view/berkeley-cs294-158-sp20/home) |
63 | | - [2019](https://sites.google.com/view/berkeley-cs294-158-sp29/home) |
64 | | - CS288: Natural Language Processing [2020](https://cal-cs288.github.io/sp20/) |
65 | | - CS285: Deep Reinforcement Learning [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) [video](https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) [2020](http://rail.eecs.berkeley.edu/deeprlcourse/) |
66 | | - CS189: Introduction to Machine Learning [2021](https://www.eecs189.org/) |
67 | | - CS188: Introduction to Artificial Intelligence |
68 | | - [2024](https://inst.eecs.berkeley.edu/~cs188/sp24/) |
69 | | - [2023](https://inst.eecs.berkeley.edu/~cs188/sp23/) |
70 | | - [2022](https://inst.eecs.berkeley.edu/~cs188/sp22/) |
71 | | - [2021](https://inst.eecs.berkeley.edu/~cs188/sp21/) |
72 | | - [2020](https://inst.eecs.berkeley.edu/~cs188/sp20/) |
73 | | - [2019](https://inst.eecs.berkeley.edu/~cs188/sp19/) |
74 | | - CS182: Designing, Visualizing and Understanding Deep Neural Networks [2021](https://cs182sp21.github.io/) |
75 | | - CS61B: Data Structures |
76 | | - [2021](https://sp21.datastructur.es/) |
77 | | - [2020](https://fa20.datastructur.es/) |
78 | | - CSC08: Foundations of Data Science |
79 | | - [2021](http://data8.org/sp21/) |
80 | | - [2020](http://data8.org/fa20/) |
81 | | - |
82 | | -* **Carnegie Mellon University** |
83 | | - 11-785: Introduction to Deep Learning |
84 | | - [2021B](http://deeplearning.cs.cmu.edu/S21/index.html) |
85 | | - [2020D](http://deeplearning.cs.cmu.edu/F20/index.html) |
86 | | - [2020B](http://deeplearning.cs.cmu.edu/S20/index.html) |
87 | | - 10-703: Deep Reinforcement Learning [2020](https://cmudeeprl.github.io/703website/) |
88 | | - 11-611: Natural Language Processing [2020](http://demo.clab.cs.cmu.edu/NLP/) |
89 | | - 10-601: Machine Learning [2015](http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml) |
90 | | - |
91 | | -* **New York University** |
92 | | - DSGA1008: Deep Learning by Yann LeCun & Alfredo Canziani |
93 | | - [2021](https://atcold.github.io/NYU-DLSP21/) |
94 | | - [YouTube](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) |
95 | | - [2020](https://atcold.github.io/NYU-DLSP20/) |
96 | | - [YouTube](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) |
97 | | - |
98 | | -* **University of Michigan** |
99 | | - EECS598-005: Deep Learning for Computer Vision |
100 | | - [2020](https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/) |
101 | | - [YouTube](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) |
102 | | - |
103 | | -* **University of Pennsylvania** |
104 | | - CIS520: Machine Learning [2020](https://alliance.seas.upenn.edu/~cis520/dynamic/2020/wiki/index.php?n=Lectures.Lectures) [notes](https://alliance.seas.upenn.edu/~cis520/dynamic/2020/wiki/index.php?n=Resources.Resources) |
105 | | - |
106 | | -* **University of Chicago** |
107 | | -CMSC35300: Mathematical Foundations of Machine Learning by Rebecca Willett |
108 | | - [2020](https://voices.uchicago.edu/willett/teaching/mathematical-foundations-of-machine-learning-fall-2020/) |
109 | | -CMSC35400: Machine Learning by Rebecca Willett & Yuxin Chen |
110 | | - [2020](https://voices.uchicago.edu/machinelearning/stats37710-cmsc35400-s20/) |
111 | | -CMSC31230: Fundamentals of Deep Learning |
112 | | - [2020](https://mcallester.github.io/ttic-31230/Fall2020/) [notes](https://mcallester.github.io/ttic-31230/) |
113 | | - |
114 | | -* **Purdue University** |
115 | | - STAT598: Machine Learning |
116 | | - [2020](https://engineering.purdue.edu/ChanGroup/ECE595/video.html) |
117 | | - [YouTube](https://nanohub.org/resources/32203) |
118 | | - |
119 | | -* **Cornell University** |
120 | | - CS4780: Machine Learning for Intelligent Systems [YouTube](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | [notes](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/) |
121 | | - [2018](http://www.cs.cornell.edu/courses/cs4780/2018fa/) |
122 | | - |
123 | | -* **University of Oxford** |
124 | | - Machine Learning [2014](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) |
125 | | - |
126 | | -* **CalTech** |
127 | | - Machine Learning [2014](http://work.caltech.edu/lectures.html) [YouTube](https://www.youtube.com/playlist?list=PLD63A284B7615313A) [iTunes](https://itunes.apple.com/us/course/machine-learning/id515364596) |
128 | | - |
129 | | - |
130 | | -**Online catalogs** |
131 | | -[MIT Open Course Ware: Computer Science Courses](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/) |
132 | | -[MIT Open Learning Library](https://openlearning.mit.edu/courses-programs/open-learning-library) |
133 | | -[Stanford Online](https://online.stanford.edu/search-catalog) |
134 | | -[Berkley Courses](https://www2.eecs.berkeley.edu/Courses/CS/) |
| 1 | +# FREE ML Courses from Top Universities |
| 2 | + |
| 3 | +> A curated collection of free Machine Learning courses from the world's leading universities |
| 4 | +
|
| 5 | +**Website:** https://kabartay.github.io/openunivcourses/ |
| 6 | +**License:** MIT |
| 7 | +**Status:** Active and maintained |
| 8 | +**Author:** Mukharbek Organokov |
| 9 | + |
| 10 | +## 🌐 [Visit the Website](https://kabartay.github.io/openunivcourses/) |
| 11 | + |
| 12 | +## 📖 About |
| 13 | + |
| 14 | +This repository contains a carefully curated collection of **free Machine Learning courses** from top-tier universities around the world. All courses are completely free to access and provide high-quality education in AI, ML, DL, RL, CV, and related fields. |
| 15 | + |
| 16 | +### Why This Collection? |
| 17 | + |
| 18 | +- **No Paywalls**: Every course is completely free |
| 19 | +- **Top Quality**: Courses from MIT, Stanford, Berkeley, CMU, and other leading institutions |
| 20 | +- **Up-to-Date**: Multiple years available for most courses |
| 21 | +- **Comprehensive**: Covers everything from beginner to advanced topics |
| 22 | +- **Accessible**: Beautiful, searchable interface with course descriptions |
| 23 | + |
| 24 | +## 📊 Statistics |
| 25 | + |
| 26 | +- **40+ Courses** from world-renowned universities |
| 27 | +- **6 Universities** represented |
| 28 | +- **100% Free** content |
| 29 | +- **Multiple Formats**: Video lectures, course notes, assignments, literature suggestions |
| 30 | +- **Regular Updates** with new courses added frequently |
| 31 | + |
| 32 | +## 🛠️ Technical Details |
| 33 | + |
| 34 | +This project is built with: |
| 35 | +- **HTML5** for structure |
| 36 | +- **CSS3** with modern features (grid, flexbox, animations) |
| 37 | +- **Vanilla JavaScript** for search and interactions |
| 38 | +- **GitHub Pages** for hosting |
| 39 | +- **GitHub Actions** for automated deployment |
| 40 | + |
| 41 | +## 📄 License |
| 42 | + |
| 43 | +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
| 44 | + |
| 45 | +## 🙏 Acknowledgments |
| 46 | + |
| 47 | +- All the universities and professors who make their courses freely available |
| 48 | +- The open-source community for tools and inspiration |
| 49 | +- Contributors who help maintain and expand this collection |
| 50 | +- **Claude (Anthropic)** and **ChatGPT (OpenAI)** for assistance with website development and documentation |
| 51 | + |
| 52 | +## 📞 Contact |
| 53 | + |
| 54 | +- **Portfolio**: [www.organokov.com](https://www.organokov.com) |
| 55 | +- **Website**: [kabartay.github.io/openunivcourses/](https://kabartay.github.io/openunivcourses/) |
| 56 | + |
| 57 | + |
| 58 | +--- |
| 59 | + |
| 60 | +<div align="center"> |
| 61 | + |
| 62 | +**[🌐 Browse Courses](https://kabartay.github.io/openunivcourses/) | [📚 Add a Course](https://github.com/kabartay/openunivcourses/issues/new) | [⭐ Star Repository](https://github.com/kabartay/openunivcourses/stargazers) | [📢 Share](https://kabartay.github.io/openunivcourses/) | [🍕 Sponsor on GitHub](https://github.com/sponsors/kabartay)** |
| 63 | + |
| 64 | +*Making quality ML education accessible to everyone* |
| 65 | + |
| 66 | +</div> |
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