| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Psychology | ||
| PSYCHOLOGICAL SCIENCE | ||
Course unit
ARTIFICIAL INTELLIGENCE
PSP5070139, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
ARTIFICIAL INTELLIGENCE
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 PSYCHOLOGICAL SCIENCE PS2192, Degree course structure A.Y. 2015/16, A.Y. 2019/20 |
 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 | |
| E-Learning website | https://elearning.unipd.it/scuolapsicologia/course/view.php?idnumber=2019-PS2192-000ZZ-2018-PSP5070139-N0 | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| Branch | PADOVA | |
| 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 |
Lecturers
| Teacher in charge | MARCO ZORZI | M-PSI/01 | |
|---|---|---|---|
| Other lecturers | ALBERTO TESTOLIN | ING-INF/03 |
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 |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| 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 (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) |
|
- Lecturing
- Laboratory
- Problem based learning
- Case study
- Use of online videos
- Loading of files and pages (web pages, Moodle, ...)
- Moodle (files, quizzes, workshops, ...)
- Matlab
