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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.

Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.

available module versions
SS 2015WS 2011/2

Basic Information

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

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

  • Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Content, Learning Outcome and Preconditions


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.
Examples are search algorithms, methods of logical inference, as well as computation of state probabilities of Bayesian networks and hidden Markov models.


IN0007 Fundamentals of Algorithms and Data Structures, IN0015 Discrete Struktures

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

Learning and Teaching Methods

The module consists of a lecture and exercise classes. The content of the lecture is presented via slides, which are completed during the lecture using the blackboard. Also, the learning progress is checked during the lecture using the survey tool Tweedback. Students are encouraged to additionally study the relevant literature. In the exercise classes, the learned content is applied to practical examples to consolidate the content of the lecture.


Slides, assignment sheets


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

Module Exam

Description of exams and course work

The duration of the written exam is 90 minutes. In the written exam students should prove to be able to identify a given problem and find solutions within limited time.
A collection of formulas and tables required to solve the given problems is provided. Students are only allowed to bring pens and a calculator (non-progammable). The questions require to solve problems mathematically and to answer questions in natural language.

Exam Repetition

The exam may be repeated at the end of the semester.

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