Additional Information

The course covers about 75% of the following topics, depending on the year:

  • mathematical foundations of machine learning (random variables and probabilities, probability distrubtions, high-dimensional spaces)
  • overview of machine learning (supervise, semi-supervised, unsupervised learning, inductive and trasductive frameworks)
  • classification algorithms: linear and non-linear algorithms(logistic regression, naive Bayes, decision tress, neural networks, support vector machines)
  • regression algorithms (least squares linear regression, neural networks, relevance vector machines, regression trees)
  • density estimation (expectation-maximization algorithm, kernel-based density estimation)
  • kernel methods (dual representations, RBF networks)
  • graphical models (Bayesian networks, Markov random fields, inference)
  • ensemble methods (bagging, boosting, random forests)
  • practical aspects in machine learning (data preprocessing, overfitting, accuracy estimation ,parameter and model selection)
  • special topics (introduction to PAC learning, sample selection bias, learning from graph data, learning from sequential data)