Machine Learning

Basic information

Workload: 

60 hours

Prerequisite: 

Linear Algebra, Statistical Modeling

Syllabus: 

Introduction to machine learning, correlation matrix and principal component analysis, linear regression with the least squares method, Bayesian classification, linear discriminant, logistic regression, cluster analysis, artificial neural networks MLP and RBF, support vector machines, combination of models, selection of variables.

 

Teaching Plan

Bibliography

Mandatory: 

•    Hastie, Trevor, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
•    Kecman, Learning and Soft Computing MIT Press, 2001.
•    R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (2nd Edition) Wiley-Interscience, 2000

Complementary: 

•    Cherkassky and F. M. Mulier Learning from Data: Concepts, Theory, and Methods, Wiley-IEEE Press; 2 edition, 2007.
•    K. P. Murphy, Machine Learning, A Probabilistic Perspective, MIT Press, 2012
•    S. Theodoris and K. Koutroumbas, Pattern Recognition, 3rd edition, Academic Press, 2006
•    C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.5.
•    B. Schölkopf, A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2001