Machine Learning II

In the subject we introduce advanced concepts in machine learning. In the first part, we concentrate of nonlinear classification and regression methods.
While in the second part, we will focus on advanced topics of non-supervised learning. In the last part of the course, we will cover transfer learning and
multitask learning.

PART 1: Nonlinear classification and regression
Kernel methods
Ensemble methods (boosting y random forests)
Gaussian Processes for classification and regression

PART 2: Unsupervised Learning
Introduction to graphical models
Latent variable models
Bayesian nonparametrics

PART 3:
Multi-task learning
Transfer learning