Introduction to Psychology
Cognitive Psychology or Consciousness (or similar courses)
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.
Students will acquire knowledge of:
aspects of the fields of artificial intelligence, robotics and neurocognition, and their potential influence on society and work environments (also in psychology);
examples of how complex behaviour and cognition emerges from different architectures of neural networks and forms of computation;
how neural networks can be trained;
the distinction between supervised, unsupervised, and reinforcement learning.
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. The exception here is that first-year bachelor students are assigned and registered for all components in the first semester or academic year by the administration of their bachelor programme. The programme will communicate to these students for which course components and for which period the registration applies.
It is mandatory for all students, including first-year bachelor students, to register for each exam and to confirm registration for each exam in My Studymap. This is possible up to and including 10 calendar days prior to the examination. You cannot take an exam without a valid pre-registration and confirmation 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 8 2-hour lectures.
There is a written exam consisting of open questions. They are based on the literature and the lectures.
The Institute of Psychology uses fixed rules for grade calculation and compulsory attendance. 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. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these three policies.
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