B027506 - MACHINE LEARNING

• Supervised learning.



• Generalized linear models. Neural networks.



• Support vector machines. Kernels.



• Computational learning theory.



• Unsupervised learning.



• Probabilistic graphical models.



• Relational learning.



• Neural networks and feature learning.



• Applications.

• [S18]O Simeone (2018), A Brief Introduction to Machine Learning for Engineers, Foundations and Trends R in Signal Processing: Vol. 12, No. 3-4, pp 200–431. DOI: 10.1561/2000000102.


• [GBC16] I. Goodfellow, Y. Bengio, and A. Courville (2016) Deep LearningMIT Press (in preparation)


• [HTF09] T. Hastie, R. Tibshirani, and J. Friedman (2009) Freely available online.


• Barber, D. (2012). Freely available online.


• Several scientific papers (see "materials" in the lectures list)

You will learn about several fundamental and some advanced algorithms for statistical learning, you will know the basics of computational learning theory, and will be able to design state-of-the-art solutions to application problems.

A good knowledge of a programming language, and a solid background in mathematics (calculus, linear algebra, and probability theory) are necessary prerequisites to this course. Previous knowledge of optimization techniques and statistics would be useful but not strictly necessary.

Lectures and practical sessions.

B024317: 9 CFU

• There is a single oral final exam. You can choose the exam topic but you are strongly advised to discuss with me before you begin working on it.


• Typically, you will be assigned a set of papers to read and will be asked to reproduce some experimental results.


• You will be required to give a short (30 min) presentation during the exam. Please ensure that your presentation includes an introduction to the problem being addressed, a brief review of relevant literature, technical derivation of methods, and, if appropriate, a detailed description of experimental work. You are allowed to use multimedia tools to prepare your presentation. You are responsible for understanding all the relevant concepts and the underlying theory.



• You can work in groups of two to carry out experimental works (three is an exceptional number that you must motivate clearly). If you do so, please ensure that personal contributions to the overall work are clearly identifiable.

B024318: 6 CFU

• There is a single oral final exam on a subset of topics (supervised learning, learning theory, grapical models)

Please see http://ai.dinfo.unifi.it/teaching/ml_2018.html