CSC 6364 Machine Learning
This course introduces machine learning in everyday life and many of its applications. We will start with probability theory, model selection, discussing dimensionality and decision theory. We will delve into probability distributions and linear models for regression and classification. We will thoroughly cover neural networks. Graphical models including Bayesian networks and Markov random fields will be discussed. Mixture and expectation models will be covered. For many of the topics discussed, everyday applications will be presented to relate the topic back to what is currently in use.