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. Supervised, unsuervised, and reinforcement learning principles are presented, and specific algorithms 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.