https://at-ceu.studyguide.timeedit.net/modules/UGST4088?type=COREThis course aims to introduce students to basic principles of machine learning algorithms. In particular, we will look into basic principles of supervised and unsupervised machine learning through exemplars of algorithms of different families: heuristic search (decision trees), ensemble learners (random forest, XGBoost), probabilistic classifiers (Naïve Bayes), lazy learning (KNN), linear and logistic regression models and fundamental principles of neural networks, as well as clustering. Throughout the course, model evaluation and interpretation of results will be emphasized.