Paolo Frasconi, DINFO, via di S. Marta 3, 50139 Firenze

email: (please do not use my address @unifi.it, it was forcibly moved to gmail by the central administration and it has all sorts of problems: messages may be replied with delay or not replied at all).

Tuesday, 10:45-12:45

Until further notice, it will be on Skype. Please with your Skype ID on the day before and I will reply with a tentative meeting time.

In this class you will learn about some fundamental statistical learning algorithms and a number of deep learning techniques. You will learn about the basics of computational learning theory, and will be able to design state-of-the-art solutions to application problems. Broad topics that are covered include: Generalized linear models, kernel methods, ensemble techniques and boosting, core deep learning methodologies, sequence learning and recurrent networks, relational learning.

A good knowledge of a programming language (preferably Python), and a solid background in mathematics (calculus, linear algebra, and probability theory) are necessary prerequisites to this course. Previous knowledge of fundamental ideas in supervised learning, probabilistic graphical models, optimization and statistics would be very useful but not strictly necessary.

[GBC16] I. Goodfellow, Y. Bengio, A. Courville.
Deep Learning. MIT
Press, 2016 (free PDF).

[A18] Charu C. Aggarwal
Neural Networks and Deep Learning. Springer,
2018 (free PDF from Unifi IP).

[HTF09] T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning. Data
Mining, Inference, and Prediction. 2nd edition. Springer, 2009 (free PDF).

[B12] D. Barber.
Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

[W13] L. Wasserman.
All of statistics:
a concise course in statistical inference. Springer Science & Business Media, 2013 (very useful if you need
to improve your general background in statistics).

[B06] Chris Bishop
Pattern Recognition and Machine Learning. Springer, 2006 (free PDF).

[SSBD14] Shai Shalev-Shwartz and Shai Ben-David.
Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014 (free PDF).

[MRT18] Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
Foundations of Machine Learning. MIT Press, Second Edition, 2018 (free PDF).

[D17] Hal Daume III
A Course in Machine Learning 2017 (free PDF).

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, the underlying theory, and the necessary background that you will usually find in the textbooks.

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.

Same as above except topics are limited to those covered in the first 2/3 of the course and you will not be asked to reimplement the methods or reproducing experimental results.

For videos please go to the Moodle-WebEx connector (UniFI credentials required).

Full text of linked papers is normally accessible when connecting from a UNIFI IP address.
Use the proxy
`proxy-auth.unifi.it:8888`

(with your credentals) if you are connecting from outside the
campus network.