The course gives 12 credits and is structured in the following four sections
(please refer to each of the sections and to the home page of the teachers for additional details,
including exams).
Each section has specific dates (check each section web-site). Book through Infostud only when you have passed all the sections (the professor marked (*) is in charge of the registration, any request for change of the modules should be addressed to him).
Section: Automated Reasoning
Prof. Paolo Liberatore
3 Credits
I Semester
Programme:
Introduction to the propositional calculus; the Davis-Putnam
procedure; tableau method for propositional logic; first-order logic;
tableau methods for first-order logic; natural deduction; sequent
calculus; Hilbert system; resolution; modal logics; tableau for modal
logics; linear temporal logics; computational tree logic; the labeling
algoritm for linear and computational tree logics.
Section:
Robot Programming
Prof. Daniele Nardi
3 Credits
I Semester
Classes start September 30th, Tuesdays 15:45-19:00, Room A5
Programme:
Robot programming requires a deep knowledge of the programming techniques and
the programming language chosen for software development.
In addition, the software for robotic applications is often built by means
of specialized development tools. The goal of the course is to discuss a
case study, in order to provide a systematic approach to robot programming.
Specifically, robot programming is addressed using C++ as basic programming
language, ROS
and NAO SDK
as development frameworks.
The target robotic platforms are a simple wheeled robot , that will be built during the
class and the NAO humanoid robot. The course addresses examples of programming
tasks in Perception, Localization and Navigation and Mapping, Actions and Plan execution, Human Robot Interaction.
Section: Visual Learning: Recognition
Prof. Barbara Caputo
3 Credits
I Semester
Classes start October 3rd, Fridays 14:00-15:30, Room A4
Programme:
This course will provide the students with a unified view on max-margin based techniques
that enable the construction of visual recognition algorithms for intelligent systems.
The course will discuss the specific requirements of vision for such systems
while introducing methods for categorization, localization, adaptive learning and scene
understanding.
Section: Introduction to Pattern Recognition
Prof. Fiora Pirri
3 Credits
II Semester
Programme:
Pattern Recognition is branch of artificial intelligence concerned with the
classification or description of observations. Pattern recognition aims to
classify data (patterns) based on either a priori knowledge or on statistical
information extracted from the patterns. The patterns to be classified are
usually groups of measurements or observations, defining points in an
appropriate multidimensional space. Topics:
1. Introduction: Basic concepts & Components of a PR system.
2. Features Extraction Methods.
3. Representation and design of Classifiers.
4. Bayesian, Discriminative and Generative approaches.
5. Applications: sound, face, ATR, fingerprints, handwritten
recognition.
Suggested readings:
1. Richard O. Duda, Peter E. Hart and David G. Stork
: Pattern Classification and Scene Analysis. J. Wiley & Sons, New York, 2nd
edition 2000. Exercise Book
2. Christopher M. Bishop, Pattern Recognition and Machine
Learning Series: Information
Science and Statistics 2006, XX, 740 p. 304