ProgramCourses › ML

Machine learning

Instructor: Luca Iocchi
Course web page:
Credits: 6
Infostud code: 1022858


The objectives of this course are to present a wide spectrum of Machine Learning methods and algorithms, discuss their properties, convergence criteria and applicability. The course will also present examples of successful application of Machine Learning algorithms in different application scenarios. The main outcome of the course is the capability of the students of solving learning problems, by a proper formulation of the problem, a proper choice of the algorithm suitable to solve the problem and the execution of experimental analysis to evaluate the results obtained.


Introduction to machine learning and probability theory. Supervised learning: K-NN, decision trees, naïve Bayes, linear and logistic regression, perceptron, neural networks, deep learning, Support Vector Machines, risk minimization. Unsupervised learning: clustering, semi-supervised learning, learning theory, VC dimension. Probabilistic representation and modeling: graphical models, Bayes nets, HMM, reinforcement learning, topic models: latent Dirichelet allocation.

Type of exam: Written test, Oral test

Reference text

  • C.M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006