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ml_uwr's Introduction

Machine Learning@University of Wrocław

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You are browsing the 2020 edition. Materials for past years are in branches: 2019.

Learning materials

Topic Learning materials
Intro to ML: Specifying problems uding data, basic terminology Slides: lectures/01-intro.pdf Notes: lectures/01-notes.ipynb (GitHub preview) or (nbviewer)
Regression: hypotheses, loss functions, regularization lectures/02-notes.ipynb (GitHub preview) or (nbviewer)
From Statistical inference to Machine Learning lectures/03-notes.ipynb (GitHub preview) or (nbviewer)
lectures/03-notes-naive_bayes-addition.ipynb (GitHub preview) or (nbviewer)
Logistic regression, numerical optimization, impact of loss function on regression solution lectures/04-05-notes.ipynb (GitHub preview) or (nbviewer)
Feature selection: wrapper methods, forawrd stagewise, L1 regularization, LARS+LASSO lectures/06-regression_var_selection_lasso.ipynb (GitHub preview) or (nbviewer)
Decision Trees: Building, pruning, Random Forests lectures/07_nodes_dt.ipynb (GitHub preview) or (nbviewer)
Boosting classifiers: AdaBoost, gradient boosting, XGBoost, Viola-Jones Face detector lectures/09_adabost.ipynb (GitHub preview) or (nbviewer)
Neural Networks and SVM lectures/10_neuralnets_kernels_svm.ipynb (GitHub preview) or (nbviewer)
Unsupervised learning: K-means, Self-Organizing maps, EM lectures/11_kMeans_SOM.ipynb (GitHub preview) or (nbviewer)
Probabilistic Graphical Models: intuitions about Kalman filter and HMM lectures/14_pgm.ipynb (GitHub preview) or (nbviewer)
Review Slides: lectures/15-review.pdf

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ml_uwr's Issues

Link for final exam doodle

There was a link to doodle about the term of the final exam. It would be nice to have it somewhere in the repo.

Out of date TODO sample in section "Estimate performance for various ks"

In this year's "Estimate performance for various ks" section, the method for preparing the training and testing datasets has changed compared to the last edition - from plain 2:1 split of the dataset to bootstrapping error estimation.

Last year sample code looked like this:
image

Now:
image

I spent some time thinking how np.split could be useful in this situation, but couldn't come up with any sensibile ideas.
Thus I think, however I might as well be just biased by my own solution, that the train_idx, test_idx = np.split(...) is not right tip for a solution.

Note: I decided not to create a pull request as suggested during the first lecture. Firstly, I am not sure if I am right. Secondly, I wouldn't want to spoil the solution.

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