Università Roma Tre

Introduction to Machine Learning: what is machine learning; what is it aimed at, what are the problems; what are the theoretical tools used; overview of the topics that will be covered during the course.

Supervised and unsupervised learning; Model representation; The cost function; The gradient descent algorithm;

Linear regression; The gradient descent for linear regression; Logistic regression; The gradient descent for logistic regression; The normal equation;

The problem of classification; The representation of the hypothesis; The cost function; The one-vs-all method; The problem of overfitting; Regularization in linear and logistic regression;

The perceptron; Le Neural Networks; The Error-back propagation algorithm; Random initialization of weights;
Model selection; The train, validation and test set; Diagnosis by bias and variance; The learning curves; Error analysis;

Support Vector Machines;

K-means clustering;

Principal Components Analysis for dimensionality reduction;

Anomaly Detection algorithms;

Recommender Systems;

Large scale machine learning systems including parallelized and map-reduce systems;

C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification (2001) John Wiley & Sons.