| Machine Learning investigates the mechanisms by which knowledge is acquired through experience. The intent of this course is to cover the primary approaches to machine learning from a variety of fields, including inductive inference of decision trees, neural network learning, statistical learning methods, generic algorithms, Bayesian methods, Information-Theoretic classification, and reinforcement learning. These various approaches will be compared and contrasted in order to determine under which conditions each is most appropriate. The course will also focus on the Theoretical concepts of machine learning such as inductive bias, the PAC learning framework, occam’s razor, models of noise, uniform convergence, and Fourier analysis methods. |
