This course aims at a balanced presentation of theoretical concepts and practical applications. The goal is to learn the motivation behind several machine learning algorithms , with an introductory mathematical treatment and then concetrate on their applications. Upon succesful completion of the course, the student should be able to apply a large number of ML algorithms to practical problems.
Course Content (Syllabus)
Supervised learning: LMS, Logistic regression, Perceptron, Gaussian discriminant analysis, Naive Bayes, Support vector machines, Model selection and feature selection.
Unsupervised learning: Clustering. K-means. EM Algorithm . Mixture of Gaussians.
Reinforcement learning and control: MDPs. Bellman equations.
Value iteration and policy iteration. Q-learning. Value function approximation. Policy search. Reinforce. POMDPs.
Learning theory: Bias/variance tradeoff, VC dimension, Online learning
Additional bibliography for study
Hastie, Friedman, and Tibshirani, The Elements of Statistical Learning, 2001
Bishop, Pattern Recognition and Machine Learning, 2006
Ripley, Pattern Recognition and Neural Networks, 1996
Mitchell, Machine Learning, 2000
Duda, Hart, and Stork, Pattern Classification, 2nd Ed., 2002
Tan, Steinbach, and Kumar, Introduction to Data Mining, Addison-Wesley, 2005.
Mardia, Kent, and Bibby, Multivariate Analysis, 1979
Sutton and Barto, Reinforcement Learning: An Introduction, MIT Press, 1998.
Bertsekas and Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996.