
MACHINE LEARNING (Numerosita' canale 1)
Prerequisites:

Basic Knowledge of Probability Theory, Statistics and Linear Algebra 
Examination methods:

Written test and takehome computer simulations 
Course unit contents:

Motivation; components of the learning problem and applications of Machine Learning.
Supervised and unsupervised learning.
PART I: Supervised Learning
Introduction: Data – Classes of models  Losses
Probabilistic models and assumptions on the data
Models, Losses and the regression function. Regression and Classification
When is a model good? Model complexity, bias variance tradeoff/generalization (VC dimension – generalization error)
Least Squares, Maximum Likelihood and Posteriors.
Models for Regression
Linear Regression (scalar and multivariate) – Stein’s paradox and Regularization – Subset Selection
Linearintheparameters models, Regularization.
Local and global models (Smoothing Kernels and NNR)
Dimensionality Reduction: Principal Component Regression, Partial Least Squares.
Classes of non linear models: Sigmoids, Neural Networks
Kernel Methods: SVM
Models for Classification
Linear Discriminant Analysis, Logistic Regression, NN, Perceptron, Naïve Bayes Classifier, SVM
Validation and Model Selection
Generalization Error, BiasVariance Tradeoff, Cross Validation, SURE.
Model complexity determination
PART II: Unsupervised learning
Cluster analysis: Kmeans Clustering, Mixtures of Gaussians and the EM estimation
Dimensionality reduction: Factor analysis, Principal Component Analysis (PCA) 

