Machine Learning
Introduction to machine learning. Nearest neighbors method. Linear regression. Polynomial regression. RBF neural networks. Logistic regression and Bayesian variant. Kernel methods. Neural networks. Model selection. Main Component Analysis. Autoencoders. k-means. Mixture of Gaussians from the EM algorithm.
Basic Information
Workload
60 hours
Requirements
Linear Algebra, Statistical Modeling
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