Course - Sub-symbolic AI Methods - IT3708
Sub-symbolic AI Methods
About
About the course
Course content
The main focus of the course is to build intelligent systems based on two key natural concepts: the brain, and evolution by natural selection. In computer-science, the analogs for these are artificial neural networks (ANNs) and evolutionary algorithms (EAs). Both methods have thousands of useful applications in fields as diverse as control theory, telecommunications, music and art. This course discusses both methods in great detail along with providing a bit of the biological basis for each.
Learning outcome
Students will get both theoretical and practical programming experience with two of the best known sub-symbolic AI methods: artificial neural networks and evolutionary algorithms.
Learning methods and activities
Regular lectures and homeworks, each lasting 2-4 weeks. The final grade is based 100% upon these homeworks, which normally consist of large programming projects along with 1 or 2 essays. In total, a semester involves 4-5 homeworks.
This course is VERY programming intensive and should not be taken by students who dislike writing code.
Group work on programming projects is acceptable, but group size cannot
exceed 2 members. However, any homework that consists solely of a report must be done individually, with no assistance given by one student to another.
Recommended previous knowledge
TDT4120 Algorithms and Data Structures and MA0301 Elementary Discrete Mathematics.
Required previous knowledge
TDT4136 Logic and Reasoning Systems, TDT4110 Information Technology, Introduction and at least one university-level course in mathematics or equivalent.
Course materials
Lecture slides, a textbook (possibly 2). Textbooks are chosen at the beginning of the semester.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| IT8801 | 7.5 sp | |
| MNFIT378 | 7.5 sp | |
| MNFIT378 | 7.5 sp |
Subject areas
- Informatics
- Technological subjects