## 2015

**Title:** Perspectives in parallel programming

**Lecturer:** Marco Danelutto, Dipartimento di Informatica

**Period:**

May 13, 11-13, Seminari W

May 15, 11-13, Seminari W

May 22, 11-13, Seminari W

(the schedule of the next lectures will be decided on May 13)

**Title:** Bayesian Machine Learning

**Lecturer:** Guido Sanguinetti, University of Edinburgh

**Place**: Computer Science Department, University of Pisa

**Period:**

- 23 February seminar room west 15 - 17;
- 24 /25 / 26 February seminar room west 9- 11;
- 27 February Laboratorio didattico Polo Fibonacci "I" 9 - 11;
- 2 March seminar room west 16 - 18;
- 3 / 4/ 5 March seminar room west 9- 11;
- 6 March Laboratorio didattico Polo Fibonacci "I" 9 - 11;

This is a rough summary for the Bayesian Machine Learning course. The main reference is D. Barber's book, Bayesian Reasoning and Machine Learning; the numbers in brackets refer to Barber's book unless explicitly stated otherwise. The book is available online from

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage

**Lecture 1**: Statistical basics. Probability refresher, probability distributions, entropy and KL divergence (Ch 1, Ch 8.2, 8.3). Multivariate Gaussian (8.4). Estimators and maximum likelihood (8.6 and 8.7.3). Supervised and unsupervised learning (13.1)**Lecture 1****Lecture 2**: Linear models. Regression with additive noise and logistic regression (probabilistic perspective): maximum likelihood and least squares (18.1 and 17.4.1). Duality and kernels (17.3).**Lecture 2****Lecture 3**: Bayesian regression models and Gaussian Processes. Bayesian models and hyperparameters (18.1.1, 18.1.2). Gaussian Process regression (19.1-19.4, see also Rasmussen and Williams, Gaussian Processes for Machine Learning, MIT Press, 2007, Ch 2. Available for download at http://www.gaussianprocess.org/gpml/).**Lecture 3**- Lecture 4: Active learning and Bayesian optimisation. Active learning, basic concepts and types of active learning (B. Settles, Active learning literature survey, sections 2 and 3, available from http://burrsettles.com/pub/settles.activelearning.pdf.) Bayesian optimisation and the GP-UCB algorithm (Brochu et al, see http://arxiv.org/abs/1012.2599).
**Lecture 4** **Lab 1**: GP regression and Bayesian Optimisation.**Lecture 5**: Latent variables and mixture models. Latent variables and the EM algorithm (11.1 and 11.2.1). Gaussian mixture models and mixture of experts (20.3, 20.4).**Lecture 5****Lecture 6**: Graphical models. Belief networks and Markov networks (3.3 and 4.2). Factor graphs (4.4).**Lecture 6****Lecture 7**: Exact inference in trees. Message passing and belief propagation (5.1 and 28.7.1).**Lacture 7****Lecture 8**: Approximate inference in graphical models. Variational inference: Gaussian and mean field approximations (28.3, 28.4). Sampling methods and Gibbs sampling (27.4 and 27.3).**Lecture 8****Lab 2**: Bayesian Gaussian mixture models.

**Title:** Verifiable voting systems and secure protocols: from theory to practice

**Lecturer:** Peter Y. A. Ryan, Université du Luxenbourg

**Period:** 6-10 July 2015 --- Sala Seminari W, 9-12

**Title:** Coinductive Methods in Computer Science (and beyond)

**Lecturer:** Filippo Bonchi and Damien Pous, ENS Lyon

**Period:** 13 -- 24 April -- all lectures will be in Sala Seminari W

**Title:** High Dynamic Range Imaging: theory and applications

**Lecturer:** Francesco Banterle, Visual Computing Laboratory, CNR Pisa

**Period:** 15-26 June 2015 -- Sala Seminari Ovest, 15-17

Reference page: http://www.banterle.com/francesco/courses/2015/hdri/index.php

**Title:** Searching by Similarity on a Very Large Scale

**Lecturer:** Giuseppe Amato, CNR Pisa

**Period:** end of September/beginning of October 2015

**Title:** Sistemi di tipi calcoli di processi e di sessione

**Lecturer:** Ilaria Castellani, Rosario Pugliese

**Period:** October 2015

**Place:** Università di Firenze, Viale Morgagni

**Title:**Scientific writing in English

**Lecturer: **Steven Shore, Department of Physics

**Period: **February 3, 4, 5, 6

In addition, each student can attend a course of the Master programme in Computer Science, for instance:

- Pierpaolo Degano: Static analysis techniques
- Ugo Montanari: Semantics and type theory
- Nadia Pisanti: Algorithms for bioinformatics