Lecturer
Course Content
In this course we will look at the foundation of what is AI. Starting with the Introduction to AI, we will look briefly at the history of AI and discuss the question “what is AI?” before we move to Problem solving by Searching. We will introduce algorithms, which consider only one or multiple agents. Additionally, we will cover Constraint Satisfaction Problems, a different type of search problems. Finally, will discuss Markov Decision Processes, where we learn about choosing the best actions in a non-deterministic world and Reinforcement Learning, where we have to learn from experience.
Content List
- Introduction to Artificial Intelligence Algorithms
- Uninformed & Informed Search
- Constraint Satisfaction Problems
- Multiagent Search, Minimax and Expectimax
- Utilities
- Markov Decision Processes
- Reinforcement Learning
Learning Goal
On completion of the course you will have learned important basic methdologies of modern Artificial Intelligence (AI) and several representative applications.
Teaching assistants
TBA
Schedule and other information
Lectures: weekly, Room: TBA, Time: TBA
Tutorials: weekly, Room: TBA, Time: TBA
Exam
- The exam duration is 90 minutes.
- The only allowed aid is a one-sided sheet of paper with handwritten notes.
Literature
Artificial Intelligence: A Modern Approach (4th Edition) by Stuart Russell and Peter Norvig.
- via TIB: https://www.tib.eu/de/suchen/id/TIBKAT:1741187877 (electronic and print version)
The lecture notes and exercises are based on the following course:
