SoSe 23: Artificial Intelligence
Grégoire Montavon
Additional information / Pre-requisites
Prerequisites:
Basic knowledge in Mathematics and Algorithms & Datastructures.
Comments
The course will introduce the basic ideas and techniques underlying the design and learning 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:
Search/Optimization Techniques
Constraint Satisfaction Problems
Bayes Decision Theory / Classifiers
Markov Decision Processes
Reinforcement Learning
Explainable AI
Format: Written exam at the end of the semester.
Suggested reading
- 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)
13 Class schedule
Additional appointments
Thu, 2023-05-11 12:00 - 14:00
Location:
Hs B (Raum B.004, 100 Pl.) (Arnimallee 22)
Regular appointments