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- Algorithms for graphical models: There are two main approaches to learning from data: model-based approaches that infer a structure like a graph, and non-parametric techniques that consider the data as observations about a parameter space.
The goal of this course is to introduce students to the scienceof machine learning, to understand the fundamentals of machine learning and toexplore some of the practical applications of machine learning. Introduction toMachine Learning is a comprehensive textbook on the subject, covering abrug array of topics not usually included in introductory machine learningtexts. Subjects include supervised learning; Bayesian decision theory;parametric, semi-parametric, and nonparametric methods; multivariate analysis;hidden Markov models; reinforcement learning; kernel machines; graphicalmodels; Bayesian estimation; and statistical testing. This textbook shows how the fundamental methods used in machine learning actually work, and explains the concepts of classification, regression, and prediction. It also covers probability theory, statistics, and the modeling of data using mathematics and computer programs. 827ec27edc