Welcome to the Introduction to AI course

The course is an introduction to the area of Artificial Intelligence and will introduce the basic ideas and techniques underlying the design of intelligent machines.

By the end of this course, you will have learned how to build autonomous (software) agents that efficiently make decisions in fully informed, partially observable and adversarial settings as well as how to optimize actions in uncertain sequential decision making environments to maximize expected reward.

Syllabus (updated/ tentative order):

• Informed search
• Uninformed search
• Adversarial search
• Local search and Optimization
• Markov Decision Processes
• Reinforcement Learning

Literature

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (http://aima.cs.berkeley.edu/)
• Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (https://mitpress.mit.edu/books/reinforcement-learning-second-edition)

Required Knowledge

Good knowledge of algorithms, data structures and mathematics. And programming of course!

ECTS

5

Active Participation Requirement

#assignements/2 & 2 projects

Schedule

The current plan is an in-person lectures and tutorials, subject to FUB regulations regarding the Coranavirus pandemic.

• Lectures: 
  • Thursdays 12:00-14:00, Arnimallee 3, Hörsaal 001.
  • Kick-off lecture on 28/04/2022.
• Tutorials: 
  • Tuesdays: 10:00-12:00, Arnimallee 6, A6/SR 032 Seminarraum
  • Tuesdays: 12:00-14:00, Takustr. 9, T9/SR 006 Seminarraum
  • Kick-off tutorial on 03/05/2022.
• Exam:
  • Thursday 21/7/2022, 12:00- 14:00, online.