Techniques in Artificial Intelligence

Module IN2062

This Module is offered by TUM Department of Informatics.

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Module version of SS 2015 (current)

There are historic module descriptions of this module. A module description is valid until replaced by a newer one.

available module versions
SS 2015WS 2011/2

Basic Information

IN2062 is a semester module in German or English language at Bachelor’s level und Master’s level which is offered in winter semester.

This Module is included in the following catalogues within the study programs in physics.

  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Content, Learning Outcome and Preconditions

Content

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 reasoniong 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.

Learning Outcome

The participants will attain capabilities to solve complex problems using fundamental methods and techniques of artificial intelligence. The techniques include agent-based problem solving, problem solving through (heuristic) search, the representation of knowledge, reasoning mechanisms, problem solving under uncertainty, action planning and machine learning.

Preconditions

IN0007 Fundamentals of Algorithms and Data Structures, IN0015 Discrete Struktures

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

ArtSWSTitelDozent(en)Termine
VU 4 Techniques in Artificial Intelligence (IN2062) Mittwoch, 14:00–16:00
Freitag, 12:45–14:15

Learning and Teaching Methods

lecture, exercise course, problems for individual study

Media

slides, assignment sheets

Literature

Stuart Russel and Peter Norvig: Artificial Intelligence - A Modern Approach, Prentice Hall

Module Exam

Description of exams and course work

In the written exam students should prove to be able to identify a given problem and find solutions within limited time.

Exam Repetition

There is a possibility to take the exam at the end of the semester.

Condensed Matter

When atoms interact things can get interesting. Fundamental research on the underlying properties of materials and nanostructures and exploration of the potential they provide for applications.

Nuclei, Particles, Astrophysics

A journey of discovery to understanding our world at the subatomic scale, from the nuclei inside atoms down to the most elementary building blocks of matter. Are you ready for the adventure?

Biophysics

Biological systems, from proteins to living cells and organisms, obey physical principles. Our research groups in biophysics shape one of Germany's largest scientific clusters in this area.