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, homeworks and a projects, along with a take-home final exam. The final grade is based 75% on the homeworks/projects and 25% on the take-home exam.
This course is VERY programming intensive, with each homework taking 2-4 weeks to complete. There are normally 4-5 such homework assignments.
Group work on homeworks is acceptable, but group size cannot
exceed 2 members. The take home exam is to be done individually, with absolutely no discussion with other students. Violation of this rule will result in a failing mark for the course.

Recommended previous knowledge

TDT4120 Algorithms and Data Structures, TDT4136 Logic and Reasoning Systems, MA0301 Elementary Discrete Mathematics

Required previous knowledge

At least one course in computer programming plus at least one university-level course in mathematics

Course materials

Lecture slides, a textbook (possibly 2). Textbooks are chosen at the beginning of the semester.

Credit reductions