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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 | suggested reading |
| 2/27 | topic 1: how can machines learn by embedding numerous features? linear support vector machine | course slides; LFD e-8.1 | |
| 3/6 | dual support vector machine | course slides; LFD e-8.2 | |
| 3/13 | kernel support vector machine | course slides; LFD e-8.3 | |
| 3/20 | soft-margin support vector machine | homework 1 announced | course slides; LFD e-8.4 |
| 3/27 | kernel logistic regression | course slides; extended reading: | |
| 4/3 | no class because of Spring Break | ||
| 4/10 | topic 2: how can machines learn by combining predictive features? blending and bagging |
course slides; extended reading: | |
| 4/17 | adaptive boosting | course slides; extended reading: | |
| 4/24 | decision tree | homework 1 due; homework 2 announced; final project announced | course slides; extended reading: |
| 5/1 | no class because of Labor Day (and yes we have a labor in this class) | ||
| 5/8 | random forest | course slides; extended reading: | |
| 5/15 | gradient boosted decision tree | course slides; extended reading: | |
| 5/22 | topic 7: how can machines learn by distilling hidden features? neural network |
course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts) | |
| 5/29 | (tentatively) deep neural network | homework 2 due; homework 3 announced | |
| 6/5 | (tentatively) convolutional neural network | ||
| 6/12 | (tentatively) recurrent neural network | ||
| 6/19 | (tentatively) variational autoencoder | ||
| 6/26 | finale and award ceremony | homework 3 due | |
| 7/3 | summer vacation started (really?) | 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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