First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
Course unit
PSP5070139, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course First cycle degree in
PS2192, Degree course structure A.Y. 2015/16, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination ARTIFICIAL INTELLIGENCE
Department of reference Department of General Psychology
E-Learning website
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit is available ONLY for students enrolled in PSYCHOLOGICAL SCIENCE

Teacher in charge MARCO ZORZI M-PSI/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines M-PSI/01 General Psychology 6.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 42 108.0 No turn

Start of activities 01/10/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2015 course timetable

Examination board
Board From To Members of the board
5 2019 31/01/2020 31/01/2020 AGRILLO CHRISTIAN (Presidente)
CUTINI SIMONE (Membro Effettivo)
TESTOLIN ALBERTO (Membro Effettivo)
4 2019 01/10/2019 30/09/2020 ZORZI MARCO (Presidente)
CUTINI SIMONE (Membro Effettivo)
TESTOLIN ALBERTO (Membro Effettivo)

Prerequisites: Basic knowledge of mathematics (high school level), including notions of linear algebra, calculus, and probability. Knowledge of statistics (“Statistical methods in psychology”) and neuroscience ("Brain and behaviour") are useful for better unserstanding some of the topics discussed in the course. Computer literacy is required for the lab practices.
Target skills and knowledge: The course presents the theoretical and computational foundations of brain-inspired artificial intelligence. The focus is on machine learning based on artificial neural networks and how this approach is used in cognitive (neuro)science for modeling perception and cognition. Laboratory classes will also introduce students to simple computer simulations with artificial neural networks.
Examination methods: Type of exam: written (exam time: 1 hour).
Modality: multiple choice questions (max 20 points) + open questions (max 6 points) + paper assignment (max 6 points).
Paper assignment: Each student will be required to write a 3/4-page essay that will be assigned during the course and that must be hand over on the day of the written exam.
Assessment criteria: The evaluation is based on the understanding of course topics and on the acquisition of the proposed concepts and methodologies.
Course unit contents: Introduction to artificial intelligence and machine learning. Artificial neural networks: mathematical formalism and general principles. Supervised learning: perceptron, delta rule, multi-layered networks and error backpropagation. Generalization and overfitting. Partially recurrent networks: learning sequential data. Unsupervised learning: associative memories and Hopfield networks, competitive learning, latent variable models, annealing, Boltzmann machines. Deep learning. Reinforcement learning. Computer simulation as a research method in cognitive (neuro)science. Connectionist models of perception and cognition.
Planned learning activities and teaching methods: Teaching is based on frontal lectures presenting the theoretical concepts, which will be also informally discussed to promote understanding through examples. The course includes lab practices (in a computer room) to explore computer simulations of artificial neural networks.
Additional notes about suggested reading: All study material will be available on the course Moodle (

* Slides of the lectures

* Selected book chapters:
- G. Houghton, “Connectionist Models in Cognitive Psychology” (2005), Chapter 1
- D. Rumelhart and J. McClelland, “The PDP book” (1986), Chapter 1
- J. Anderson, “An Introduction to Neural Networks”, Chapter 12

* Selected articles:
- Elman, “Finding structure in time.” Cognitive Science (1990)
- Jacobs, and Grainger. "Models of visual word recognition: Sampling the state of the art." Journal of Experimental Psychology: Human perception and performance (1994)
- Zorzi, Testolin, and Stoianov. "Modeling language and cognition with deep unsupervised learning: a tutorial overview." Frontiers in Psychology (2013)
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
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