Machine Learning

The course lectures will present the theory and practice of the following:
- Machine Learning & Pattern Classification: the general concepts of Machine Learning and Patter Recognition are introduced within the framework of Statistical Decision Theory with reference to the bias-variance trade of and the Bayes classifier;
- Generalized Linear Regression: linear methods for regression will be presented and discussed introducing different techniques (e.g., Linear Regression, Ridge Regression, K-Nearest Neighbors Regression, Non Linear Regression, etc.) and the most common methodologies for model validation and selection (e.g., AIC, BIC, cross-validation, stepwise feature selection, Lasso, etc.).
- Linear and Non Linear Classification: generative and discriminative techniques for classification will be described and discussed (e.g., Logistic Regression, Linear and Quadratic Discriminant Analysis, K-Nearest Neighbors, Perceptron Rule and Support Vector Machines, etc.). Metrics for classifiers evaluation and comparison are presented in this part of the course (e.g., accuracy, precision, recall, ROC, AUC, F-measure, Matthew coefficient).
- Unsupervised Learning: the most common approaches to unsupervised learning are described mostly focusing on clustering methods such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, etc.
These topics will be presented both from a theoretical perspective and a practical one via implementations in the general purpose programming language Python.