Course Description This is the summer edition of CS229 Machine Learning that was offered over 2019 and 2020. CS229 provides a broad introduction to statistical machine learning (at an intermediate / advanced level) and covers supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical ); and reinforcement learning among other topics. The structure of the summer offering enables coverage of additional topics, places stronger emphasis on the mathematical and visual intuitions, and goes deeper into the details of various topics.
Event | Date | Description | Materials and Assignments |
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Introduction and Pre-requisties review (3 lectures) | |||
Lecture 1 [YouTube] | 6/24 |
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Lecture 2 [YouTube] | 6/26 |
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Lecture 3 [YouTube] | 6/28 |
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Supervised Learning (8 lectures) | |||
Lecture 4 [YouTube] | 7/1 |
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Lecture 5 [YouTube] | 7/3 |
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Lecture 6 [YouTube] | 7/5 |
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Lecture 7 [YouTube] | 7/8 |
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Lecture 8 [YouTube] | 7/10 |
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Lecture 9 [YouTube] | 7/12 |
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Lecture 10 [YouTube] | 7/15 |
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Lecture 11 [YouTube] | 7/17 |
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Theory (2 lectures) | |||
Lecture 12 [YouTube] | 7/19 |
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Lecture 13 [YouTube] | 7/22 |
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Reinforcement Learning (2 lectures) | |||
Lecture 14 [YouTube] | 7/24 |
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Lecture 15 [YouTube] | 7/26 |
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Unsupervised Learning (3 lectures) | |||
Lecture 16 [YouTube] | 7/29 |
Unsupervised Learning
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Lecture 17 [YouTube] | 7/31 |
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Lecture 18 [YouTube] | 8/2 |
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Miscellaneous Topics (3 lectures) | |||
Lecture 19 | 8/5 |
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Lecture 20 | 8/7 |
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Lecture 21 | 8/9 |
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Recap and wrap-up (2 lectures) | |||
Lecture 22 | 8/12 |
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Lecture 23 | 8/14 |
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Final | 8/16 | ||
Other Resources
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