News
- We replace the exercise by a lecture on Friday, December 05, 2014. In turn, we will have two exercises in the next week on Friday, December 12, 2014.
- There is no lecture on Thursday, December 04, 2014 due to Dies Academicus.
- Change of lecture hall on Thursday, November 27, 2014. We will be in Rudolf Mößbauer Hörsaal PH 2501 since there is a guest lecture from the Red Bull Racing formula 1 team, which requires to put a real formula 1 car in the lecture hall. This is only possible in our lecture hall.
- We have lecture instead of the exercise on Friday, October 10, 2014. Later, a lecture will be replaced by the missing exercise.
- The lecture starts Thursday, October 09, 2014
Exam
- Regular exam: Mon 02.02.2015, 10:30 - 12:00. Seating:
| lecture hall |
last name |
| MW 0001 |
Achard - Job |
| MW 2001 |
Jonischkeit - Schlicker |
| MW 2001 Empore |
Schlosser - Taskiran |
| MW 0350 |
Thapa - Yildirim |
| MW 1050 (Zeichensaal) |
Zappe - Zwickl |
- Post exam review of the regular exam: Mon 02.03.2015, 14:00 - 16:00, room MI 03.07.023.
- Repetition exam: Tue 31.03.2015, 08:00 - 09:30. Seating:
| lecture hall |
last name |
| MW 0001 |
Aicher - Salman |
| MI Hörsaal 1 |
Sarikaya - Zhang |
- Post exam review of the repetition exam: Fri 24.04.2015, 10:00 - 12:00, room MI 02.07.034.
Description
The course gives an overview of application areas and techniques in Artificial Intelligence. The course introduces the principles and techniques of Artificial Intelligence based on the textbook of Russell and Norvig (see
below). The course covers the following topics:
- design principles and specification mechanisms for rational agents;
- problem solving using heuristic search: heuristic search techniques, optimizing search;
- problem solving using knowledge-based techniques: logic and inference techniques; reasoning about space and time; representation of ontologies; representation and reasoning in the common sense world.
- problem solving using uncertain knowledge and information: basic concepts of probability and decision theory; Bayesian Networks; planning with Markov decision problems.
- action planning: automatic generation of partially ordered action plans; planning and execution
- machine learning: learning decision trees; inductive learning; probably approximately correct learning; reinforcement learning.
Material
The material is provided through the
moodle website.
Literature
