Max amount of FITech students: 30
This course presents the student overview of some of the basic AI theories and applications with practicality in mind. In the course projects, students get some experience in programming AI based applications and using especially AI based search methods.
Course content: Intelligent agent types, uninformed search methods, informed (heuristic) search, local search, constraint satisfaction problems, adversarial search, uncertainty handling, probabilistic reasoning, utility, machine learning, decision networks, Markov decision process, reinforcement learning, applications
After completing the course, students
- know the basic search strategies that can be applied in problem solving and optimization
- understand how search-based decisions are made in game-like competitive applications
- know the basic principles of probabilistic reasoning in artificial intelligence systems
- know how rational decision making under uncertainty can be formulated using utility theory
- understand the fundamentals of machine learning and how some of the established methods can be applied to problems in AI
- are familiar with advanced AI applications of perception and robotics and how probabilistic inference and machine learning can be used in these settings.
Course is organised online. Students have a possibility to do an electronic exam using the EXAM software.
You can get a digital badge after completing this course.
AI tekoäly datankäsittely data-analyysi prosessointi koneoppiminen
Further information about the studies
Contact person for applications