Machine learning involves the study of computational methods that solve complex problems through learning from data examples. The objective of this course is to teach basic and advanced machine learning techniques based on supervised and unsupervised learning. 

Special emphasis will be given to techniques based on probabilistic modeling and statistical estimation. Specific algorithms that will be studied in detail are the following: Regression and logistic regression using linear models and neural networks. Deep learning methods. Generative classification using k nearest neighbors and Bayes classifiers. Support vector machines and kernel methods. Clustering using k-means and mixtures models. The expectation-maximization algorithm. Dimensionality reduction and latent variable modeling using principal component analysis (PCA), probabilistic PCA and factor analysis. Hidden Markov models and probabilistic graphical models.