Artificial Intelligence I
A.Y. 2024/2025
Learning objectives
The course aims to provide a general introduction to the Artificial Intelligence area considering the main and current sub-fields that characterize it. We will deal with classes of problems and associated solution methods at the basis of many of today's techniques that apply Artificial Intelligence in the real world.
Expected learning outcomes
The course will provide fundamental knowledge of the main areas of Artificial Intelligence, how they relate to the real world and to each other. The student will learn to recognize the problems with respect to which the techniques discussed in the course can provide a solution approach and to set their application. The ability to orientate oneself efficiently in the various areas of the discipline will be transmitted, providing a solid basis for targeted and autonomous in-depth studies.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
The program is structured as follows:
- introduction to Artificial Intelligence, applications, research areas, and communities;
- autonomous and rational agents;
- automatic problem solving: formalization of the graph search problem, uninformed search, heuristic search;
- presence of adversaries: games and optimal strategies, game tree search;
- Constraint Satisfaction Problems: definition and resolution with search algorithms;
- uncertainty and sequential decisions: Markov Decision Processes;
- Reinforcement Learning;
- introduction to Machine Learning paradigms and basic approaches: decision trees, regression, and classification, non-parametric methods;
- neural networks: shallow and deep learning.
- introduction to Artificial Intelligence, applications, research areas, and communities;
- autonomous and rational agents;
- automatic problem solving: formalization of the graph search problem, uninformed search, heuristic search;
- presence of adversaries: games and optimal strategies, game tree search;
- Constraint Satisfaction Problems: definition and resolution with search algorithms;
- uncertainty and sequential decisions: Markov Decision Processes;
- Reinforcement Learning;
- introduction to Machine Learning paradigms and basic approaches: decision trees, regression, and classification, non-parametric methods;
- neural networks: shallow and deep learning.
Prerequisites for admission
The course does not require any previous knowledge of the subject. However, for a better and easier understanding of the topics covered, basic knowledge of linear algebra, algorithms and probability and statistics are recommended. Having successfully attended the courses of Discrete mathematics, Algorithms and Data Structures, and Statistics and Data Analysis is a more than a sufficient guarantee.
Teaching methods
The theory part is given with frontal lectures where slides are presented. Slides are made available in PDF format through the myAriel platform. Attendance is recommended.
Teaching Resources
The course is based on the topics presented in the book "Artificial Intelligence: A Modern Approach" by Peter Norvig and Stuart J. Russell (Fourth edition, vol. 1 and 2)
For further support, slides and other supplementary material are provided during the course on the myAriel platform.
For further support, slides and other supplementary material are provided during the course on the myAriel platform.
Assessment methods and Criteria
The exam consists of a written test lasting at most 3 hours where exercises and open questions with short answers are proposed.
The exercises require the application of the techniques discussed in class to problems of complexity appropriate to the duration of the test. Open questions assess knowledge of basic concepts and how they can be applied to problem-solving in the real world.
During the test, it is not allowed to consult any material.
The vote is out of thirty and will be communicated through the myAriel platform.
The assessments will take into account the mastery of techniques, correctness, and elegance of the solutions, clarity of presentation, knowledge of the concepts, and the ability to apply them in new settings. The exam and its evaluation will not be differentiated based on frequency.
The exercises require the application of the techniques discussed in class to problems of complexity appropriate to the duration of the test. Open questions assess knowledge of basic concepts and how they can be applied to problem-solving in the real world.
During the test, it is not allowed to consult any material.
The vote is out of thirty and will be communicated through the myAriel platform.
The assessments will take into account the mastery of techniques, correctness, and elegance of the solutions, clarity of presentation, knowledge of the concepts, and the ability to apply them in new settings. The exam and its evaluation will not be differentiated based on frequency.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Basilico Nicola
Shifts:
Turno
Professor:
Basilico NicolaEducational website(s)
Professor(s)