Machine learning 1000-2N09SUS

1. Introduction: Preliminaries and basic notions in machine learning
2. Supervised learning methods: Classification problem, classifier evaluation methods, basic classification algorithms: Na?ve Bayes, KNN, decision rules, decision trees, decision forest. Artificial Neural Networks.
3. Learning function and concept approximation: "Gradient descent" and "Back Probagation" algorithms, logistic regression, SVM classifiers.
4. Computational Learning Theory (COLT): PAC model in learning theory, VC dimension, bagging & boosting methods. Multiclass to binary reduction, cost-sensitive learning, ranking learning;
5. Unsupervised Learning: Hierarchical Clustering, K-means, Expectation Maximization (EM) method. Principal component analysis: PCA. MDS. pPCA. Independent component analysis: ICA.
6. Reinforcement learning: MDP (Markov decision processes), Bellman equations, TD(?) learning (Temporal-difference learning) and Q-learning