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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 |
| 2/19 | topic 1: how can machines learn by embedding numerous features? linear support vector machine |
course slides; LFD e-8.1 | |
| 2/26 | dual support vector machine kernel support vector machine |
course slides; LFD e-8.2 course slides; LFD e-8.3 |
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| 3/5 | soft-margin support vector machine | homework 1 announced | course slides; LFD e-8.4 |
| 3/12 | kernel logistic regression support vector regression |
course slides; extended reading:
extended reading: |
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| 3/19 | topic 2: how can machines learn by combining predictive features? blending and bagging |
course slides; extended reading: |
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| 3/26 | adaptive boosting decision tree |
homework 1 due | course slides; extended reading: course slides; extended reading: |
| 4/2 | no class because of Spring Break | homework 2 announced; final project announced | |
| 4/9 | random forest | course slides; extended reading: |
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| 4/16 | gradient boosted decision tree topic 7: how can machines learn by distilling hidden features? neural network |
course slides; extended reading: course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts) |
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| 4/23 | (shallow explanation of) deep learning | course slides; LFD e-7.6 | |
| 4/30 | (shallow explanation of) deep learning radial basis function network |
homework 2 due; homework 3 announced |
course slides; LFD e-6.3 |
| 5/7 | no class because instructor is at ICLR | ||
| 5/14 | radial basis function network matrix factorization |
course slides | |
| 5/21 | modern deep neural network: activation and optimization | homework 3 due; homework 4 announced | extended reading:
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| 5/28 | modern deep neural network: optimization and regularization | extended reading:
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| 6/4 | convolutional neural network | extended reading: | |
| 6/11 | machine learning for modern artificial intelligence | homework 4 due | course slides (re-use talk slides at Institute of Information Science, Academia Sinica, Taipei, Taiwan, November 2018) |
| 6/18 | finale and award ceremony | summary slides; final project award presentation slides | |
| 6/25 | 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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