Course

Introduction, linear classification, perceptron update rule, Perceptron convergence, generalization, Maximum margin classification,Classification errors, regularization, logistic regression,Linear regression, estimator bias and variance, active learning Non-linear predictions, kernels, Kernel regression, kernels, Support vector machine (SVM) and kernels, kernel optimization.

Model selection, Model selection criteria, Description length, feature selection, Combining classifiers, boosting, Boosting, margin, and complexity, Margin and generalization (EM) algorithm, EM, regularization, clustering, Clustering, Spectral clustering, Markov models, Hidden Markov models (HMMs), Bayesian networks, Learning Bayesian networks, Probabilistic inference, Current problems in machine learning.

1. Machine Learning, Tom Mitchell, McGraw Hill, 1997
2. Christopher, M. Bishop. Pattern Recognition and Machine Learning, Springer-Verlag New York, 2016.
3. Duda, Richard, Peter Hart, and David Stork, “Pattern Classification” Second Edition, New York, NY: Wiley-Interscience, 2000.
4. Hastie, T., R. Tibshirani, and J. H. Friedman, “The Elements of Statistical Learning: DataMining, Inference and Prediction”, New York, Springer, 2001.