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
Type of course
Course coordinators
Bibliography
1. Bishop, "Pattern Recognition and Machine Learning", 2007
2. Hastie, Tibshirani and Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction", 2001
3. MacKay, "Information Theory, Inference, and Learning Algorithms", 2003.
4. Mitchell, "Machine Learning", 1997
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: