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 first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course is an introduction to programming and to artificial intelligence (AI) for psychologists. It has two main objectives: to teach students the basics of programming through the use of the Matlab language and to provide them with theoretical and technical tools inspired by artificial intelligence (AI) to represent mental processes. The main topics covered are as follows: the basic principles of programming, vector and matrix computation in Matlab, symbolic approaches in AI illustrated with a few systems, the theoretical framework of neural networks, deep networks, large language models (LLM), challenges posed to connectionist approaches, artificial life (genetic algorithms). The different concepts are presented using examples and computer applications mainly written in Matlab
More details (slide presentations, course videos, Matlab programs and various notes) can be found in the 2022-2023 course website
Why an introduction to AI for psychologists?
- AI has become a societal phenomenon. Psychologists must have an elementary knowledge of it in order to be able to appreciate its impacts and its benefits.
- AI is a source of concern 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 a 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, and in particular recent developments with LLMs like GPT3 and GPT4, 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 objective of the course is twofold. It must provide the student with the basic concepts of programming languages and bring him/her useful elements for the formalization 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, hybrid learning)
Face-to-face course
Additional information:
Face-to-face, using smartboard. Video versions of the courses (audio and presentation screens) will also be available.
Recommended or required readings
Copies of slides, notes on specific topics and an optional reading list are provided.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions ) AND oral exam
Written work / report
Additional information:
Exam(s) in session
May-June exam session
- In-person
oral exam
August-September exam session
- In-person
oral exam
Written work / reports
Mandatory work - programming exercises, systems analysis, construction of neural networks and genetic algorithms - will have to be returned to participate in the exams.
Additional information:
Participation in computer labs, presentation of personal work and oral exam with prior written preparation on the subjects covered in the course. Students will be judged on the mandatory work, 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)
Organisational remarks and main changes to the course
Contacts
D.Defays (ddefays@uliege.be)
J.Sougné (jsougne@ulg.ac.be)