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 # ml_uwr
 Materials for my Machine Learning course taught at the University of Wroclaw.
 
-### Exam
+# Learning materials
+|            | Topic                                     | Learning materials                                                                                                                                                           |
+|------------|-------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Lecture 1  | Introduction to ML                        | [slides](lectures/01-intro.pdf), Murphy Ch. 1                                                                                                                                              |
+| Lecture 2  | Prob. review                              | [slides](lectures/02-prob-review.pdf) notebook [1](lectures/02_binomial_polls.ipynb) [2](lectures/02_entropy.ipynb), Murphy Ch. 2                                                          |
+| Lecture 3  | Decision Trees                            | [TUM course](https://argmax.ai/ml-course/lecture-02-trees/) [CS229](http://cs229.stanford.edu/notes/cs229-notes-dt.pdf), Murphy Ch. 16                                                      |
+| Lecture 4  | Random Forests                            | [Breiman paper](https://link.springer.com/article/10.1023/A:1010933404324) [Breiman's tutorial](https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf), Murphy Ch. 5 & 16        |
+| Lecture 5  | Adaboost                                  | [notebook](lectures/05_adabost_demo.ipynb) [notebook](lectures/08-gradient-boosting-demo.ipynb)   Murphy Ch. 16 [xgboost slides](https://homes.cs.washington.edu/~tqchen/data/pdf/BoostedTree.pdf)                                                                          |
+| Lecture 6  | Linear and Logistic Regression            | notebook [1](lectures/06-least-squares.ipynb) [2](lectures/06-logistic-regression.ipynb) [CS229](http://cs229.stanford.edu/notes/cs229-notes1.pdf)                                                                                    |
+| Lecture 7  | More on Regression, Regularization, Lasso | [notes](lectures/07-inprogress-lecture.pdf) [notebook](lectures/07-regressions_robust_lasso.ipynb)                                                                           |
+| Lecture 8  | Discrimantive vs Generative classifiers   | [notebook](lectures/08-generative_discriminative.ipynb) [naive bayes](lectures/03_naive_bayes.ipynb) [cs229 notes](http://cs229.stanford.edu/notes2019fall/cs229-notes2.pdf) Murphy Ch. 4 |
+| Lecture 9  | SVM #1                                    | [cs229 notes](http://cs229.stanford.edu/notes2019fall/cs229-notes3.pdf) [tutorial](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/svmtutorial.pdf)      |
+| Lecture 10 | SVM #2, Review of Sup. Learning           | [slides](lectures/10-supervised_review.pdf)                                                                                                                                  |
+| Lecture 11 | kMeans                                    | [notebook](lectures/11-kMeans-SOM.ipynb)  [CS229 kMeans](http://cs229.stanford.edu/notes/cs229-notes7a.pdf) [CS229 EM1](http://cs229.stanford.edu/notes/cs229-notes7b.pdf) [CS229 EM2](http://cs229.stanford.edu/notes/cs229-notes8.pdf) |
+| Lecture 12 | PCA                                       | [notebook](lectures/12_PCA.ipynb)   [CS229](http://cs229.stanford.edu/notes/cs229-notes10.pdf)                                                                                                                                         |
+| Lecture 13 | ICA + NMF                                 | [notebook ICA](lectures/13_ICA.ipynb) [notebook NMF](lectures/13-nmf.ipynb)  [CS229](http://cs229.stanford.edu/notes/cs229-notes11.pdf)                                                                                                |
+| Lecture 14 | Intro to PGMs and HMMs                    | Bishop Ch. 8 & 13                                                                                                                                                            |
+| Lecture 15 | Company presentations                     |                                                                                                                                                                              |
+
 
-Final exam poll:
-https://doodle.com/poll/2iykztedmyzaqfa6