Computational Psychology and Artificial Intelligence

About

How does the brain process information, make decisions, and learn? Computational Psychologists seek to answer these questions by using algorithms and mathematical models to simulate and analyse the mechanisms behind mental processes. The field has been highly influential on Artificial Intelligence research and development, as data scientists attempt to convincingly recreate human thought, speech, and behaviour in machines, a challenge Alan Turing called the ‘Imitation Game’. Introducing Computational Psychology, Computational Neuroscience, and AI, this course offers a fascinating insight into these exciting and forward-looking interconnected fields of research.

The course begins with an introduction to Computational Psychology, exploring the ways in which process-based computational models may be used to represent the working of the human brain, employing algorithms to simulate aspects of cognition and predict behaviour. We shall then turn to how such models correlate with neurobiology, the actual network of cells and signals which constitutes the brain, investigating neuron models, how neural networks perform computations, and neuropsychological theories of learning. Finally, we shall look at the ways in which computational approaches to psychology and neuroscience have influenced, and been influenced by, developments in Artificial Intelligence. We will discuss the physical symbol systems hypothesis and human and artificial cognitive architectures, before considering future developments in computational psychology and artificial intelligence, such as the possibility of machine consciousness and Artificial General Intelligence.

From analysing models of mental processes to exploring machine intelligence, join an LMH Summer Programme and discover this important and evolving field of research.

Target group

This course would suit students who are interested in the scientific study of mental processes and their analysis through computational methods.

- Basic knowledge of calculus, linear algebra, and probability theory is required.
- Some prior study of Cognitive Psychology is beneficial but not essential.
- Prior study of Computer Science, Programming, Artificial Intelligence, or Machine Learning is not required.

Course aim

By the end of this course, you will:

- Understand the differences between supervised and unsupervised learning and the fundamentals of clustering.
- Be able to utilise a range of algorithms and techniques for unsupervised, self-supervised, and semi-supervised learning.
- Be able to evaluate the efficacy of real-world applications of deep unsupervised learning across various domains.
- Be able to demonstrate familiarity with the current state of research into deep unsupervised learning.

Fee info

For full list of items included in the programme fee, please visit our website.