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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 |
| 02/23 | course introduction; topic 1: when can machines learn? basics of machine learning |
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| 03/02 | the learning problems | |
| 03/09 |
topic 2: why can machines learn? feasibility of learning |
homework 1 announced |
| 03/16 | theory of generalization | |
| 03/23 |
topic 3: how can machines learn? linear models |
homework 2 announced |
| 03/30 | beyond basic linear models | homework 1 due |
| 04/06 |
topic 4: how can machines learn better? combatting overfitting |
homework 3 announced |
| 04/13 | combatting overfitting (2) | homework 2 due |
| 04/20 | putting it altogether: support vector machine | homework 4 announced; final project announced |
| 04/27 | soft-margin support vector machine | homework 3 due |
| 05/04 |
topic 5: how can machines learn by combining predictive features? bagging and boosting |
homework 5 announced |
| 05/11 | decision tree ensembles | homework 4 due |
| 05/18 |
topic 6: how can machines learn by distilling hidden features? deep learning fundamentals |
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| 05/25 | only two hours of class because of PAKDD machine learning soundings |
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| 06/01 | machine learning in practice / finale | homework 5 due |
| 06/08 | 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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