Duration
30h Th
Number of credits
| Master in psychology (120 ECTS) | 3 crédits |
Lecturer
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course is an introduction to artificial intelligence (AI) for psychologists. The main topics are: AI history, networks (Bayesian, semantic, slipnet), modeling of intellectual fluidity, theoretical framework of neural networks, the challenges facing connectionist approaches and their correspondents in the organization of the mind, deep networks, representation of forms and their matching, artificial life. The different concepts will be presented with examples and matlab applications.
More details (powerpoint presentations, course videos, matlab programs and various notes) can be found in the 2018-2019 course website
Why an introduction to AI for psychologists?
- AI has become a societal phenomenon and is a source of for psychologists. The discipline is becoming increasingly important in the intellectual and economic life, it impacts or will impact more and more professional life, training and tutorials.
- AI, just like mathematics and computer science, provides a toolkit for generating and testing models; it is likely to be in the future for psychologists as useful as statistics are today.
- Some familiarity with AI and programming is an definite advantage in the job market whether for the development of intelligent systems, or for their training and assessment of their human-like behavior.
- With AI, a new kind of thought is emerging. The psychologist who is, among others, a specialist in scientific approaches to mental phenomena cannot ignore it.
Learning outcomes of the learning unit
The course must provide to the student a series of basic concepts used in the study of cognitive phenomena and give her useful elements for the formalization of a number of intellectual processes.
Prerequisite knowledge and skills
Basic mathematical concepts (graph, function, basic operations on matrix) - General cognitive Psychology
Planned learning activities and teaching methods
The course takes different forms: lectures, programming exercises with matlab.
Mode of delivery (face-to-face ; distance-learning)
Face-to-face, using smartboard and recording of audio and presentation screens.
Recommended or required readings
Copies of slides, notes on specific topics and an optional reading list are provided.
Assessment methods and criteria
Participation in computer labs, presentation of a personal work and oral exam with prior written preparation on the subjects covered in the course. Students will be judged on their ability to identify appropriate quantitative approaches to the study of the mind, to perceive their limits and to apply accurately and rigorously the methods presented in the course.
Work placement(s)
Organizational remarks
Contacts
D.Defays (ddefays@uliege.be)
J.Sougné (jsougne@ulg.ac.be)
Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session
Teaching methods implemented : distance-learning
An online course is available at the following address: https://fileserve.fplse.uliege.be/seethread2.php?id=760&code=mYvTHvEm8XpVHM0SZlunKFkJJywXqvSwIN165Dje&m=0
This course includes videos for the different lessons of the course, the corresponding powerpoints presentations, exercises or illustrations in Matlab / Octave with a video if appropriate, additional notes.
Assessment subjects
The models
History of AI: the first symbolic systems
History of AI: reasoning and problem solving.
History of AI: associations, analogies; learning; what is possible today
Neural networks. Programming a perceptron
Learning: PMC and Matlab programming
Boltzmann machine, autoencoders. Matlab programming
Feature detector, deep learning. RBM programming
Genetic algorithms. Matlab programming
Artificial life and complements to neural networks: limits, examination of the hidden layers
TRACX neural network and its applications
Assessment methods
A written report will be submitted on June 11 (sent by email to J. Sougné). It will take the form of Matlab / Octave programme of a variation on a programme presented and discussed in class (either a neural network or a genetic algorithm). Different types of variations will be proposed on Thursday 23 April.
An individual interview will be organized. Questions will be asked on the subject covered in the course with 15 minutes of preparation and on the programme submitted on June 11. It will last approximately 60 ', including preparation. Possible types of questions are included in powerpoint presentations.
Contacts
Daniel Defays : ddefays@uliege.be
Jacques Sougné : jsougne@uliege.be
Adaptation of teaching commitments following the COVID-19 pandemic for the Aug-Sept 2020 session
Assessment subjects
Identical to the 2020 May-June session
Assessment methods
Identical to the 2020 May-June session. The report should be sent by August 20 at the latest.
Contacts
Daniel Defays : ddefays@uliege.be
Jacques Sougné : jsougne@uliege.be