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Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning.
| date | syllabus | todo/done |
| 09/23 | course introduction; topic 1: when can machines learn? basics of machine learning |
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| 09/30 | the learning problems | |
| 10/07 |
topic 2: why can machines learn? feasibility of learning |
homework 1 announced |
| 10/14 | theory of generalization | |
| 10/21 |
topic 3: how can machines learn? linear models |
homework 1 due; homework 2 released |
| 10/28 | beyond basic linear models | |
| 11/04 |
topic 4: how can machines learn better? combatting overfitting |
homework 3 released |
| 11/11 | combatting overfitting (2) | homework 2 due |
| 11/18 | machine learning in practice | final project released |
| 11/25 |
topic 5: how can machines learn by embedding numerous features? primal and dual support vector machine |
homework 3 due; homework 4 released |
| 12/02 | soft-margin kernel support vector machine | |
| 12/09 | no class because of NeurIPS 2021 | homework 4 due; homework 5 released |
| 12/16 |
topic 6: how can machines learn by combining predictive features? decision tree, bagging, and random forest |
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| 12/23 | adaptive boosting and gradient boosting | homework 5 due; homework 6 released |
| 12/30 |
topic 7: how can machines learn by distilling hidden features? deep learning fundamentals | |
| 01/06 | deep learning optimization | homework 6 due |
| 01/13 | deep learning regularization and finale | |
| 01/20 | no class and good luck with your final project | final project due |
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Last updated at CST 13:08, October 04, 2023 Please feel free to contact me:
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