Course Instructor: Adina Magda Florea

Machine learning is concerned with computer programs that automatically improve their performances based on past data and solutions. The course covers the theory and practice of machine learning from a variety of perspectives. By the end of the course, the students will now methods and algorithms for supervised learning and unsupervised learning, inductive learning, statistical learning, reinforcement learning, learning with artificial neural networks and genetic algorithms, learning set of rules. The learning applications that will be presented cover several domains, for example pattern recognition, statistics, optimization and control, decision theory, planning and prediction, data mining.

Syllabus:

• Introduction.
• Basic concepts.
• Inductive learning.
• Concept learning.
• Computational learning theory.
• PAC learning.
• Statistical learning.
• Bayesian learning.
• Reinforcement learning.
• Artificial neural networks.
• Genetic algorithms and genetic programming.
• Knowledge in learning.
• Inductive logic programming (ILP).
• Learning set of rules.
• Learning algorithms for data mining.