The course aims to provide an introduction to Artificial Intelligence (AI) by covering the main topics ranging from search and logic, to decision-making and learning. The course is highly recommended to whomever is interested in the exciting topic of AI for the current studies, and possibly for a future job in research or industry.

The course is organized into 15 lectures with the following topics:

Lecture 00: Introductory remarks;
Lecture 01: Intelligent agents;
Lecture 02: Classical search;
Lecture 03: Advanced search;
Lecture 04: Propositional logic;
Lecture 05: First-order logic;
Lecture 06: Knowledge representation;
Lecture 07: Planning;
Lecture 08: Uncertainty quantification and probabilistic reasoning;
Lecture 09: Probabilistic reasoning over time;
Lecture 10: Decision theory;
Lecture 11: Game theory;
Lecture 12: Machine learning;
Lecture 13: Deep learning;
Lecture 14: Reinforcement learning;
Lecture 15: Demo exam.

At the end of the course, the students will have acquired the required theoretical and empirical knowledge about classic and modern Artificial Intelligence (AI) approaches. They will have a good knowledge of the core problems and goals of AI, its current state-of-the-art, and its future challenges. They will be able to conduct their M.Sc. theses on topics related to AI, and they will have good starting knowledge of AI for a future career in research or industry.

The evaluation consists of a written exam.
The exam is in English and lasts 2 hours. There will be an exam at the end of the Winter Semester and one at the beginning of the Summer Semester. It will consist of open questions and/or quick application of algorithms by hand.

The course is organized into 15 lectures and 15 exercise sessions. The lectures will provide both theoretical and practical perspectives on the respective topics. The exercise sessions will aim at strengthening the knowledge of the topics presented in the lectures through a quick refresh and guided exercises. Some of the exercise sessions will be used to host invited lectures given by experts about cutting-edge topics of AI, e.g., Robotics and Virtual Reality, explaining them more in detail compared to the lecture. Through the expert knowledge of the invited speakers, the invited lectures aim to provide a rich knowledge of the topics that will shape the future of AI, with a particular focus on research aspects and their impact on society.

IMPORTANT: This course is mutually exclusive with "Künstliche Intelligenz 1", since they overlap significantly. This means that students can register either for the exam of this course or for "Künstliche Intelligenz 1". This course also has a slight overlap with "Künstliche Intelligenz 2", but students are allowed to register for both these exams if desired.
Students are also allowed to follow this course and have it registered as "Künstliche Intelligenz 1", if needed.

The main reference book for the course is:
- Stuart, Russell, and Norvig Peter. "Artificial Intelligence A Modern Approach Third Edition." (2010). (https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf).

Additional references are:
- Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006. (https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf);
- Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009;
- Peterson, Martin. An introduction to decision theory. Cambridge University Press, 2017;
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. (http://incompleteideas.net/book/the-book.html).