First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
PSYCHOLOGICAL SCIENCE
Course unit
ARTIFICIAL INTELLIGENCE
PSP5070139, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
PSYCHOLOGICAL SCIENCE
PS2192, Degree course structure A.Y. 2015/16, A.Y. 2017/18
N0
bring this page
with you
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination ARTIFICIAL INTELLIGENCE
Department of reference Department of General Psychology
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit CANNOT be chosen as Optional Course unit

Lecturers
Teacher in charge MARCO ZORZI M-PSI/01
Other lecturers ALBERTO TESTOLIN 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

Mode of delivery (when and how)
Period First semester
Year 2nd Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours of
Individual study
Shifts
Lecture 6.0 42 108.0 No turn

Calendar
Start of activities 02/10/2017
End of activities 19/01/2018

Syllabus
Prerequisites: The topics discussed in the second part of the course are also covered, in a different way, in the courses “General Psychology” and “Neuropsychology”. Knowledge of the content of these courses is required as introduction to the study of connectionist models of cognition. Computer literacy and basic notions of linear algebra are also required.
Target skills and knowledge: The course presents theory and practice of connectionist modelling with artificial neural network. The discussion of various types of neural networks and learning algorithms is followed by examples of application to the cognitive (neuro)sciences for modelling cognitive functions in both normal and pathological states.
Examination methods: Type of exam: written.
Modality: multiple choice questions and open questions + paper assignment.
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. The grade given to the paper will weight for 20% of the final score.
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. 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. Connectionis models of normal and impaired cognitive functions.
Planned learning activities and teaching methods: Teaching is based on frontal lectures covering the theory and practice classes on neural network modelling.
Additional notes about suggested reading: All study material will be available on the course Moodle (https://elearning.unipd.it/dpg/):

* 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)