News

• On Friday, October 30, we have 2x45 mins exercise to balance out the missing exercise in the first week.
• On Friday, October 16, we have 2x45 mins lecture since we do not yet have enough material for the first exercise. Towards the end of the semester, we will have an exercise instead of a lecture to balance this change.
• The lecture starts Wednesday, October 14, 2015
• Exam Review on Thursday, March 3, 2016 from 14.00 - 16.00 in room 02.07.023

Exam

The exam will be a 90 min written exam. You will not be able to take any written material into the exam, but a formula sheet will be provided. You should take a calculator and a pen (not a pencil).

• The exam is on 08.02.2016, 13:30-15:00
• The repetition exam is on 06.04.2016, 08:30-10:00

Seating arrangement for the first exam is by last name and is as follows:

• MW 0001, Gustav-Niemann-Hörsaal, Garching: A - Harrer
• MW 1801, Ernst-Schmidt-Hörsaal, Garching: Härtl - Lohmaier
• MI Hörsaal 1, Garching: Lopez - Sailer
• 101, Interims Hörsaal 1, Garching: Sajwan - Wehe
• 102, Interims Hörsaal 2, Garching: Weidinger - Zwirglmaier

Seating for the repetition exam is in MW 2001, Rudolf-Diesel-Hörsaal Garching and is separated by surname:

• Abdullah - Schaule are in the main lecture hall (MW 2001)
• Scheidl - Weiditz are in the balcony (MW 2001 Empore). Please be aware there is a separate entrance for this; you have to go up some stairs.
• Wilming - Zeilhofer, please report to the invigilators before the start of the exam to be seated.

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.
• Last year's moodle website (for long-term preview) is here.

Literature

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall, 2009. ISBN 0-13-604259-7.
• German edition: Russel/Norvig: Künstliche Intelligenz: Ein moderner Ansatz, 3. Auflage, Pearson, 2012