CSE446 - Machine Learning

Sewoong Oh
University of Washington

linear regression

9/25, overview
9/27,9/30,10/2 regression
- demo: demo1_1nn_lin [.html], [.ipynb],
- demo: demo2_lin [.html], [.ipynb]
- demo: demo3_diabetes [.html], [.ipynb]
- reading: [Murphy 1.1-1.4,7.1-7.3] [HTF 1, 2.3.1]
10/4, 10/7, bias-variance tradeoff
- demo: demo4_tradeoff [.html], [.ipynb]
- reading: [Murphy 7.1-7.3] [HTF 2.3.1]
10/9, 10/11, 10/14 regularization (ridge regression)
- demo: demo5_ridge [.ipynb], [.html]
- reading: [Murphy 7.10-7.12] [HTF 3.1-3.2,7.1-7.2]
10/16, 10/18 non-quadratic regularization (lasso regression)
- demo: demo6_lasso [.ipynb],[.html]
- reading: [Murphy 4.1-4.2,4.4] [HTF 3.4.2,7.10]
- extra video recording (required to watch): coordinate descent 1 coordinate descent 2

classification

10/21, 10/23 logistic regression
- demo: demo8_classification [.ipynb], [.html]
- reading: [Murphy 8.5.2, 14.5] [HTF 4.1-4.2,4.4-4.5,12-12.2]

10/28 perceptron and Support Vector Machine (SVM)
- demo: interactive demo
- reading: [Murphy 8.5.0, 8.5.4, 14.5] [HTF 4.5, 12-12.2, 4.1-4.3, 18.7]

unsupervised learning

neural network

11/18, 11/20, 11/22, 11/25, 11/27 neural network
- demo: demo10_nn [.ipynb], [.html]
- demo: demo11_conv [.ipynb], [.html]