Materials for my Machine Learning course taught at the University of Wroclaw.
| Topic | Learning materials | |
|---|---|---|
| Lecture 1 | Introduction to ML | slides, Murphy Ch. 1 |
| Lecture 2 | Prob. review | slides notebook 1 2, Murphy Ch. 2 |
| Lecture 3 | Decision Trees | TUM course CS229, Murphy Ch. 16 |
| Lecture 4 | Random Forests | Breiman paper Breiman's tutorial, Murphy Ch. 5 & 16 |
| Lecture 5 | Adaboost | notebook notebook Murphy Ch. 16 xgboost slides |
| Lecture 6 | Linear and Logistic Regression | notebook 1 2 CS229 |
| Lecture 7 | More on Regression, Regularization, Lasso | notes notebook |
| Lecture 8 | Discrimantive vs Generative classifiers | notebook naive bayes cs229 notes Murphy Ch. 4 |
| Lecture 9 | SVM #1 | cs229 notes tutorial |
| Lecture 10 | SVM #2, Review of Sup. Learning | slides |
| Lecture 11 | kMeans | notebook CS229 kMeans CS229 EM1 CS229 EM2 |
| Lecture 12 | PCA | notebook CS229 |
| Lecture 13 | ICA + NMF | notebook ICA notebook NMF CS229 |
| Lecture 14 | Intro to PGMs and HMMs | Bishop Ch. 8 & 13 |
| Lecture 15 | Company presentations |
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