ARTIFICIAL INTELLIGENCE
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:
- Introduction to AI
- Graph search
Implementation example of search algorithms: Python code (notebook) - Knowledge representation using logical languages
- Knowledge representation and inference under uncertainty: Bayesian networks
- Introduction to Machine Learning: see the web site https://unica-ai.github.io/
Exercises
- Graph search
- Knowledge representation using logical languages
- Knowledge representation and inference under uncertainty: Bayesian networks
- Machine learning: see the web site https://unica-ai.github.io/
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"
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Tue., January 16, 14:00
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Thu., February 1, 14:00
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Tue., February 20, 14:00
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Tue., March 26, 14:00 – only for students enrolled as "fuori corso"
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Tue., June 11, 9:00
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Tue., June 25, 9:00
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Tue., July 16, 9:00
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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
- Artificial Intelligence: A Modern Approach
Authors’ web site of the textbook, including a number of links to AI resources - Italian Association for Artificial Intelligence (AIxIA)
A non-profit academic organization for promoting study and research on AI. AI*IA yearly awards prizes to AI-related theses and grants to its members to attend AI-related events
Teaching evaluation forms
Teaching evaluation forms for the past three academic years are available (in Italian):
- academic year 2016/2017
- academic year 2017/2018
- academic year 2018/2019
- academic year 2019/2020
- academic year 2021/2021
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