ARTIFICIAL INTELLIGENCE: KNOWLEDGE REPRESENTATION AND PLANNING

The course is designed to offer the foundational basis characterizing the curriculum in Data Science. The course aims at offering a introduction to the principles, the techniques and the main applications of Artificial Intelligence. The goal is to allow the students to gain practical competences in the choice and implementation of a solution based on artificial intelligence techniques.

1. Knowledge and understanding
1.1. acquire the main models of knowledge representation and automatic extraction;
1.2. acquire the main models for automatic classification and understand the relationship with the data representation;

2. Ability to apply knowledge and understanding
2.1. acquire the ability to apply the models studied to real problems;
2.2. acquire the ability to critically assess the performance and behavior of a model applied to a concrete problem;

3. Judgement
3.1. ability to understand which characteristics of the various models of artificial intelligence are best suited to a given problem;
3.2. ability to critically evaluate the theoretical characteristics of the proposed models;

This class requires knowledge on calculus, linear and non-linear optimization, probability, and statistical inference.

Problem solving and planning:
Informed search and exploration
Constraint satisfaction problems
Adversarial search.
Knowledge representation and reasoning:
Logic and theorem proving
Expert Systems
Semantic Networks
Learning:
Vector Model
Discriminative/generative classification
unsupervised classification
kernel methods
feature synthesis/selection
manifold learning
similarity & structural representations

S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (second edition). Prentice-Hall, 2002.

The teaching is aimed at allowing the student to gain practical competences in the choice, development, and analysis of intelligent systems.

Testing is performed through a project

The project, to be developed during the class in a series of in itinere assignments or as a signle final exam, requires an analyssi of the behaviour an dperformacen of the algorithms used, thus allowing the student to acquire and demonstrate practical competences in the choice, development, and analysis of intelligent systems.

The teaching is aimed at allowing the student to gain practical competences in the choice and critical analysis of the techniques and methodologies of artificial intelligence.
Testing is performed through a series of projects in itinere and a final oral examination or a single project and an oral examination. The goal of the projects is to allow the student to immediately apply and verify the acquired competences, and in particular the critical analysis of the behavior and applicability of the algorithms studied.

English

oral