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Artificial Intelligence


Entry requirements

  • Introduction to Psychology

  • Cognitive Psychology or Consciousness (or similar courses for students outside of the bachelor programme Psychology) Students outside of the bachelor programme Psychology (excl. Exchange students) who have taken a similar course, need to contact the study advisers of Psychology to assess if they fullfil the entry requirements (deadline: 14 days before the start of the course).


Artificial intelligence (AI) is a growing field of study with its techniques finding widespread implementation. The study of artificial intelligence raises important but difficult questions about how the human brain can produce intelligent behavior. And if the human brain can produce intelligence, why wouldn’t artificial brains be able to do the same?

This course gives a basic introduction into several aspects of AI, ranging from more traditional approach in AI (known as good old-fashioned AI or GOFAI) to ones inspired by neural processing in the brain (e.g. deep learning). We will start by looking at the history and foundations of the field of artificial intelligence, its relationship with cognitive science and psychology, and some key milestones. We will discuss how AI distinguishes itself from the related fields of computer science, psychology, and mathematics. Several graph traversal algorithms and heuristics (branch-and-bound, hill climbing) are discussed, as well as broadly applicable techniques such as backtracking.

We will then touch on the field of cognitive robotics, illustrating how implementing simple decision rules in a physical embodiment can create artificially intelligent robots. The field of machine learning, and its three forms (supervised learning, unsupervised learning, reinforcement learning) are explained using several examples.

In the last part of the course we will talk about the applications of AI in psychology and the workplace in general. The course will touch on several mathematical concepts, including the concepts of information, pattern classification, vector representation, perceptrons, and linear separability and its relation to pattern classification.

Course objectives

At the end of the course, the student can:

  • describe how the field of artificial intelligence emerged from mathematics, cognitive psychology, and cybernetics;

  • give high-level explanations of several algorithms, including graph traversal, expert system reasoning, reinforcement learning, and evolutionary algorithms;

  • explain how artificial neural networks can be trained and evolved;

  • distinguish between the machine learning techniques of supervised, unsupervised, and reinforcement learning;

  • explain how robotics is different from other fields of AI;

  • discuss the potential influence of AI on society and work environments.


For the timetable of this course please refer to MyTimetable



Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register up to 5 days prior to the start of the course.


You must register for each exam in My Studymap at least 10 days before the exam date. Don’t forget! For more information, see the enrolment procedure.
You cannot take an exam without a valid registration in My Studymap.

Carefully read all information about the procedures and deadlines for registering for courses and exams.

Students who take this course as part of a LDE minor or a premaster programme, exchange students and external guest students will be informed by the education administration about the current registration procedure.

Mode of instruction

The course consists of seven 2-hour lectures.

Assessment method

There will be a final written exam. Questions are based on the literature and the lectures.

The Institute of Psychology uses fixed rules for grade calculation. It also follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. All students are required to take and pass the Scientific Integrity Test with a score of 100% in order to learn about the practice of integrity in scientific writing. Students are given access to the quiz via a module on Brightspace. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these two policies.

Reading list

  • Capita selecta.

Contact information