| Date | Content | Reading | Slides |
|---|---|---|---|
| Introduction to ML, Maximum Likelihood, Linear methods, Overfitting, Regularization, Optimization | |||
| 9/27 | Welcome/Overview, MLE |
Probability review: Murphy 2.1-2.4, 2.6, 2.8, 3.1-3.2 Statistics review, maximum likelihood: Murphy 4.2 |
slides, annotated slides |
| 10/2 | Linear regression |
Linear algebra review: Murphy 7.1-7.3 Matrix calculus review: Murphy 7.8 Maximum likelihood regression: Murphy 4.2 Linear regression: Murphy 11-11.2 |
slides, annotated slides |
| 10/4 | Linear regression with basis functions, Cross-validation |
Maximum likelihood regression: Murphy 4.2 Linear regression: Murphy 11-11.2 |
slides, annotated slides Linear regression demo .ipynb, .html, diabetes.txt Polynomial regression demo .ipynb, .html |
| 10/9 | Bias Variance Trade-off |
Bias Variance trade-off: Murphy 4.7.6 |
slides, annotated slides Bias Variance tradeoff demo .ipynb, .html |
| 10/11 | Regularization, sparsity |
Ridge regression: Murphy 11.3-11.4 |
slides, annotated slides |
| 10/16 | Lasso, Gradient descent |
Lasso regression: Murphy 11.4 Gradient descent: Murphy 8-8.2.1 |
slides Gradient descent demo .ipynb, .html |
| 10/18 | Gradient descent, Prediction pitfalls, stochastic gradient descent |
Gradient descent: Murphy 8-8.2.1 |
slides, annotated slides Gradient descent demo .ipynb, .html |
| 10/23 | Convexity, Stochastic Gradient descent |
Gradient descent: Murphy 8-8.2.1 Stochastic gradient descent: Murphy 8.4-8.4.4 |
slides, annotated slides |
| 10/25 | Classification, logistic regression |
Logistic regression: Murphy 10-10.2.4, 10.3-10.3.3 |
slides, annotated slides |
| Non-linear methods | |||
| 10/30 | Kernel methods |
Kernels: Bishop 6-6.2, Murphy 17, 17.1, 17.3.4, 17.3.9 |
slides, annotated slides |
| 11/1 | Midterm |
|
|
| 11/6 | Bootstrap, neural network basics |
Bootstrap: Efron and Hastie 10.2, 11-11.2 Neural Networks : Murphy 13-13.4.3 |
slides, annotated slides
Tensorflow playground |
11/8 | Non-parametric methods, Nearest neighbors |
Neural Networks : Murphy 13-13.4.3 Nearest neighbors: Murphy 16.1 |
slides, annotated slides
Tensorflow playground |
11/13 | More non-parametric methods, Tree-based |
Nearest neighbors: Murphy 16.1 Trees, Random Forrests: Murphy 18 Gradient Boosting Trees: Murphy 18 |
slides, annotated slides |
| Unsupervised Learning | |||
| 11/15 | SVD, PCA |
Singular value decomposition , PCA, Autoencoders: Murphy 20.1, 20.3, 20.4.6, 22.1 |
slides |
| 11/20 | SVD, PCA |
Singular value decomposition , PCA, Autoencoders: Murphy 20.1, 20.3, 20.4.6, 22.1 |
slides, annotated slides | 11/22 | More matrix decompositions, k-means, GMM |
Singular value decomposition , PCA, Autoencoders: Murphy 20.1, 20.3, 20.4.6, 22.1 K-means, GMM: Murphy 21.3-21.5 |
slides, annotated slides |
| Domain specific models | |||
| 11/27 | Generative vs. Driscriminative, Feature extraction, Domain specific models |
Self-supervised and transfer learning: Murphy 19 CNNs: ZLLS 7-8 |
slides, annotated slides |
| 11/29 | CNNs, Self-supervised learning |
Self-supervised and transfer learning: Murphy 19 CNNs: ZLLS 7-8 |
slides |
| 12/4 | Sequence models and text processing | Sequence models: ZLLS 9-11 | slides |