Fumera Giorgio
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Giorgio Fumera

ARTIFICIAL INTELLIGENCE

22 December 2023

MSc programs in Computer Engineering, Cybersecurity and Artificial Intelligence, and in Electronic Engineering

Academic year 2023/2024

This course is held under the MSc programs in Computer Engineering, Cybersecurity and Artificial Intelligence, and in Electronic Engineering. It provides basic knowledge of some of the main approaches, methods, and application fields of Artificial Intelligence, under a computer engineering perspective. The followinqg areas are covered:

  • graph search
  • knowledge representation using logical languages
  • Bayesian networks for knowledge representation and reasoning under uncertainty
  • introduction to machine learning: supervised classification, decision trees, artificial neural networks

Prerequisites: elements of discrete mathematics (combinatorics), computer architecture, at least one programming language.

ECTS credits: 6

Syllabus

  • Introduction to the course (1 h)
  • Graph search (lectures: 8 h, exercises: 3 h)
    Formulation of graph search problems: state space, actions, goal, path cost, search tree.
    Uninformed search strategies: breadth-first, depth-first; notes on other search strategies: uniform cost, depth-limited, iterative-deepening depth-first, bidirectional.
    Informed search strategies: best-first search, greedy search, A*; heuristic functions.
    Properties of search algorithms: optimality, completeness, computational (time and space) complexity.
  • Knowledge representation using logical languages (lectures: 6h, exercises: 3 h)
    Introduction: logical languages, inference algorithms.
    Propositional logic and first-order logic: syntax and semantics.
    Propositional inference: model checking algorithm.
  • Bayesian networks for knowledge representation and reasoning under uncertainty (lectures: 7 h, exercises: 2 h)
    Fundamentals of probability theory.
    Bayesian networks: structure and meaning; inference algorithms.
  • Introduction to machine learning (lectures: 22 h, exercises: 8 h)
    Introduction and general concepts; supervised classification problems.
    Decision trees; the ID3 learning algorithm.
    Artificial neural networks: the perceptron and its learning algorithm; feed-forward multilayer architecture and back-propagation learning algorithm.
    Ensemble methods.
    The scikit-learn Python library.

Timetable

The course is held in the first semester at the LIDIA Software Laboratory:

  • Tuesday, 15-18
  • Thursday, 9-12

Teaching material

Textbook

S.J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, 4th Ed., Pearson, 2021 (or previous editions)
Several copies of the English and Italian editions are available at the Faculty Library, and at other libraries of our University and outside (S.J. Russell, P. Norvig, Intelligenza Artificiale: un approccio moderno, UTET, 1998; Pearson Education Italia, 2005).

Lecture slides and exercises

Extended version of the lecture slides:

Exercises

Machine learning software

The Python scikit-learn library will be used during the course.

Grading

The exam consists of a written test, possibly a supplemental oral test, if deemed necessary by the instructor to better assess student's knowledge, and of a computer project:

  • the written test is made up of open questions and exercises about all the course topics
  • the computer project aims at deepening student's knowledge of one of the course topics by solving specific problem instances through a computer program which can be either implemented by the students or already available; projects can be made individually or by groups of two students

The computer project should be agreed with the instructor. Possible topics will be discussed during lectures.

To pass the exam a pass mark in both the written test and the project is required; the final grade (expressed in the numeric range 18-30) will be a weighted average of the two grades: 2/3 for the written test, 1/3 for the project. To pass the written test, a basic knowledge of all the course topics is required.

Dates of the next examination sessions (written test)

  • Wed., Nov. 8, 14:30, room IAI_IA (formerly room AB) – only for students enrolled as "fuori corso"

  • Tue., January 16, 14:00

  • Thu., February 1, 14:00

  • Tue., February 20, 14:00

  • Tue., March 26, 14:00 – only for students enrolled as "fuori corso"

  • Tue., June 11, 9:00

  • Tue., June 25, 9:00

  • Tue., July 16, 9:00

  • Tue., Sept. 10, 9:00

Registration through the esse3 portal is required (deadline: two days before the exam)

Recommended reading

  • D.R. Hofstadter, Gödel, Escher, Bach: an eternal golden braid, Basic Books, 1979
  • S. Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, Profile Books Ltd, 2019
  • K. Crawford, The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, Yale University Press. 2021
  • N. Cristianini, The Shortcut – Why intelligent machines do not think like us, CRC Press, 2023

Useful links

Teaching evaluation forms

Teaching evaluation forms for the past three academic years are available (in Italian):

How to contact the instructors

Ambra Demontis
Phone: 070 675 5755
E-mail: name DOT surname AT unica DOT it

Giorgio Fumera
Phone: 070 675 5754
E-mail: surname AT unica DOT it

Department of Electrical and Electronic Engineering (DIEE), building M, 3rd floor

Most recent update: December 22, 2023

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