Computational Affective Modelling II

Introduction to Artificial Intelligence (5cr), or similar MOOCs worth 5cr.

After Part II of the course, the student additionally

- understands how limitations in information-processing capacity and the amount of data used in learning are related to affective phenomena

- In particular, is able to discuss the implications of uncertainty and uncontrollability in agent design, and their relation to affective phenomena and emotions

- has a general understanding of how interventions can be designed to reduce negative affect

- has the expertise necessary to use these ideas and theories in areas such as HCI, computational neuroscience, or safe AI.

Study materials: All material is provided in the MOOC, which includes text pages, multimedia content (e.g. figures and short recorded lectures), and multiple-choice questions (the last serving both for learning and assessment).

Literature: While the MOOC contains everything, it is quite precisely based on an e-book written by Aapo Hyvärinen, see https://www.cs.helsinki.fi/u/ahyvarin/painintl/(Avautuu uuteen välilehteen)  , so that this Part II of the course corresponds to Parts II and III of the book. The factual contents of the MOOC and the e-book are basically equivalent, except that some details (basically the footnotes) are left out of the MOOC, while multimedia content will be created for the MOOC merely as pedagogical devices without increasing the factual content.

Completion methods: Independent study (MOOC)

Assessment practices and criteria: Assessment based on multiple-choice questions in the MOOC system.

Activities and methods in support of learning: This is a MOOC and based on independent study of material on the MOOC web pages. 

Target groups: Primarily, advanced students (starting from 3rd year Bachelor’s) of data science and computer science. Secondarily, students in any related disciplines since the prerequisites are kept quite low, especially International Bachelor’s of Science and Liberal Arts and Sciences (typically in 3rd year). The course is not obligatory in any program. Being a MOOC, it is open to “everybody” under the usual conditions of UH MOOCs.

Teaching period: This MOOC will be available any time. 

Recommended time or stage of studies: 3rd year Bachelor's of CS, or any year of DS/CS master. For non-CS students, typically an optional course in a Master’s degree, but in International Bachelor’s in Science and Liberal Arts and Sciences, advanced (e.g. 3rd year) Bachelor’s students.

Study module: This is part of the specialization studies module "Cognition in Brain and Machines" in the data science master’s program.

Expiry of studies: to be decided

Language of Instruction: English