Machine Learning

Course number: 
4541.666
Year: 
Graduate
Credit: 
3

Machine learning is a research field of artificial intelligence that attempts to build problem-solving systems that continuously improve their performance through experience and data observation from the environment. This course investigates the fundamental, theoretical and practical issues in machine learning, and their application examples. The principles of supervised, unsuervised, and reinforcement learning are presented and specific algorithm are discussed for symbolic rules, decision trees, memory-based learning, neural networks, genetic algorithms, Bayesian networks, hidden Markov models, kernel methods, and other up-to-date machine learning methods.