Students
- know and understand the most important classes of machine learning models and algorithms for classification problems (k2);
- know how to select, configure, and run appropriate machine learning algorithms for a given problem (k3);
- know how to set up systematic learning experiments (k3);
- an how to evaluate and interpret the results (k5).
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- Fundamental concepts of supervised learning;
- Important classes of classification models and learning algorithms: Bayes classification and Bayes error; density estimation; nearest-neighbour classification; standard classifiers in machine learning (decision trees, Naive Bayes, feedforward neural networks, support vector machines, ensemble methods);
- empirical evaluation of classifiers;
- clustering and mixture models;
- Markov processes and Hidden Markov Models.
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