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 |
9/14 | course introduction | course slides | |
9/16 | topic 1: when can machines learn? the learning problem | course slides; LFD 1.0, 1.1.1, 1.2.4 | |
9/21 | learning to answer yes/no | course slides; LFD 1.1.2, 3.1 | |
9/23 | types of learning | course slides; LFD 1.2 | |
9/28 | no class because of Mid-Autumn Festival | ||
9/30 | feasibility of learning | homework 1 announced | course slides; LFD 1.3 |
10/5 | topic 2: why can machines learn? training versus testing | course slides; LFD 2.0, 2.1.1 | |
10/7 | theory of generalization | course slides; LFD 2.1.2 | |
10/12 | the VC dimension | course slides; LFD 2.2 | |
10/14 | noise and error | homework 1 due; homework 2 announced | course slides; LFD 1.4 |
10/19 | topic 3: how can machines learn? linear regression | course slides; LFD 3.2 | |
10/21 | logistic regression | course slides; LFD 3.3 | |
10/26 | linear models for classification | course slides; LFD 3.3 (for SGD part only) | |
10/28 | nonlinear transformation | homework 2 due; homework 3 announced | course slides; LFD 3.4 |
11/2 | topic 4: how can machines learn better? hazard of overfitting | course slides; LFD 4.0, 4.1 | |
11/4 | regularization | course slides; LFD 4.2 | |
11/9 | validation | course slides; LFD 4.3 | |
11/11 | three learning principles | homework 3 due; homework 4 announced | course slides; LFD 5 |
11/16 | topic 5: how can machines learn by embedding numerous features? linear support vector machine | course slides; LFD e-8.1 | |
11/28 | dual support vector machine | final project announced | course slides; LFD e-8.2 |
11/23 | kernel support vector machine | course slides; LFD e-8.3 | |
11/25 | soft-margin support vector machine | homework 4 due; homework 5 announced | course slides; LFD e-8.4 |
11/30 | kernel logistic regression | course slides; extended reading: | |
12/2 | support vector regression | course slides; extended reading: | |
12/7 | topic 6: how can machines learn by combining predictive features? blending and bagging |
course slides; extended reading: | |
12/9 | adaptive boosting | homework 5 due; homework 6 announced | course slides; extended reading: |
12/14 | decision tree | course slides; extended reading: | |
12/16 | random forest | course slides; extended reading: | |
12/21 | gradient boosted decision tree | course slides; extended reading: | |
12/23 | topic 7: how can machines learn by distilling hidden features? neural network |
homework 6 due; homework 7 announced | course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts) |
12/28 | deep learning | course slides; LFD e-7.6 | |
12/30 | radial basis function network | course slides; LFD e-6.3 | |
1/4 | no class to enjoy your holidays and homework/project better (instructor office hour in R314) | ||
1/6 | matrix factorization | homework 7 due; homework 8 announced | course slides; |
1/11 | no class to fight for the final project in the last hours | ||
1/13 | finale and award ceremony | course slides | |
1/18 | no class because winter vacation started (really?) | ||
1/20 | no class because winter vacation started (really?) | homework 8 due; final project due |
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