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Machine Learning@University of Wrocław

Welcome!

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