### An Introduction to Bisimulation and Coinduction (IBC)

Prof. Davide SangiorgiLab. Focus, Inria (France) and University of Bologna (Italy)

**Summary:**

A fundamental concern in concurrency theory is establishing
when two processes are "the same", i.e., undistinguishable
to an external observer interacting with them. This notion,
called, behavioural equivalence, is the basis upon which a
theory of processes can be developed. In the lectures I
will review the main forms of behavioural equivalence. I
will devote more time to bisimulation, for its importance
and mathematical properties. The discovery of bisimulation
has spurred the study of coinduction, today widely used
also outside concurrency theory. I will therefore also
discuss the general meaning of coinduction, with examples
from various computer science fields.

The lectures will refer to the draft text
An introduction to Bisimulation and Coinduction by
Davide Sangiorgi. This draft is being made available by the
author exclusively for the students of this course.
**Please do not criculate!**

### Learning Theory: Statistical and Game-Theoretic Foundations (LT)

Prof. Nicolò Cesa-BianchiUniversità degli Studi di Milano (Italy)

**Summary:**

Machine learning is a discipline concerned with the design
of algorithms that can predict future data based on past
observations. Machine learning has become a standard tool
in applications involving intelligent data analysis
(extraction of patterns from data, data categorization and
clustering, decision-making, adaptive control) and has been
applied to domains such as vision, natural language,
biology, web, and others. In the course we explore the
double nature, statistical and game-theoretic, of machine
learning theoretical foundations. We introduce and
investigate a number of basic topics in learning theory,
including mistake bounds and risk bounds, empirical risk
minimization, online linear optimization, compression
bounds, linear learning, overfitting and regularization.
The ultimate goal of the course is to provide a sound
mathematical framework within which one can answer to
questions such as: What is the weakest set of assumptions
about the data ensuring the existence of a learning
algorithm? How much training data should I provide in order
to attain a certain predictive performance?

Prerequisites: probability and statistics, linear algebra, optimization.

### Advanced Algorithms for Massive Data Sets (MDS)

Prof. Paolo FerraginaUniversity of Pisa (Italy)

**Summary:**

Modern information retrieval and data mining applications
for the Web (but not only that!) need to carefully cope
with the new algorithmic challenges posed by the processing
of large datasets and by the architectural features of the
memory hierarchy of current computers. These issues force
algorithm designers to address simultaneously data
compression (fitting more data in the faster/smaller memory
levels) and cache-friendly data access (exploiting the
accessing features of memory levels). Every lecture will
follow a problem-driven approach that starts from a real
software-design problem, abstracts it in a combinatorial
way (suitable for an algorithmic investigation), and then
introduces basic and sophisticated algorithmic solutions
aimed at minimizing the use of computational resources like
time, space, communication, I/O, energy consumption, etc..
The theoretical investigation will go hand-in-hand with
some algorithm-engineering considerations.

Prerequisites: Basic course on Algorithms, basic notions of probability theory.

More information (including reading list and slides) are available at the course site.

### Foundations of Advanced Networking (FAN)

Prof. Francesco Lo PrestiUniversity of Rome "Tor Vergata" (Italy)

**Summary:**

The course covers advanced fundamental design and
implementation principles of computer networks. We will
first review today Internet architecture and revisit the
design and implementation principles of Internet, ATM and
telephony networks. We will present different protocol
mechanisms/techniques as soft state, signaling,
randomization, multiplexing and discuss their role in
networking. We will then illustrate router design and
architecture and scheduling algorithms. We will then review
Intra-Domain and Inter-Domain routing and the BGP protocol
and policies. The course will then cover traffic
engineering and resources allocation. We will review TCP
congestion control and illustrate recent results which show
how it can be regarded as distributed network optimization
problem. Finally we will consider the role of network
measurements. In particular, we will address workload
models, traffic and/or topology classification and present
network tomographic techniques for the estimation of
traffic demand matrices and network characteristics.