Machine Learning

This course aims to provide students with an in-depth introduction to two main areas of Machine Learning: supervised and unsupervised. The course covers some of the main models and algorithms for regression, classification, and clustering. Topics will include linear and logistic regression, regularization, Maximum Likelihood Estimation (MLE), probabilistic (Bayesian) inference, Support Vector Machines (SVMs), kernel methods, Artificial Neural Networks (ANNs), clustering, and dimensionality reduction. The course will use primarily the Python programming language and assumes familiarity with linear algebra, probability theory, and programming in Python.