de | en

Techniques in Artificial Intelligence (IN2062)

Course 240927786 in WS 2021/2

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 4 SWS
Organisational Unit Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll)
Lecturers Matthias Althoff
Josefine Gaßner
Adrian Kulmburg
Eivind Meyer
Gerald Würsching
Dates Fri, 13:00–14:30, MW 2001
Thu, 16:00–18:00, MW 0001

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks - Task environments and the structure of intelligent agents. - Solving problems by searching: breadth-first search, uniform-cost search, depth-first search, depth-limited search, iterative deepening search, greedy best-first search, A* search. - Constraint satisfaction problems: defining constraint satisfaction problems, backtracking search for constraint satisfaction problems, heuristics for backtracking search, interleaving search and inference, the structure of constraint satisfaction problems. - Logical agents: propositional logic, propositional theorem proving, syntax and semantics of first-order logic, using first-order logic, knowledge engineering in first-order logic, reducing first-order inference to propositional inference, unification and lifting, forward chaining, backward chaining, resolution. - Bayesian networks: acting under uncertainty, basics of probability theory, Bayesian networks, inference in Bayesian networks, approximate inference in Bayesian networks. - Hidden Markov models: time and uncertainty, inference in hidden Markov models (filtering, prediction, smoothing, most likely explanation), approximate inference in hidden Markov models. - Rational decisions: introduction to utility theory, utility functions, decision networks, the value of information, Markov decision processes, value iteration, policy iteration, partially observable Markov decision processes. - Learning: types of learning, supervised learning, learning decision trees. - Introduction to robotics: robot hardware, robotic perception, path planning, planning uncertain movements, control of movements, robotic software architectures, application domains.
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SemesterTitleLecturersDates
WS 2020/1 Techniques in Artificial Intelligence (IN2062) Althoff, M. Klischat, M. Mayer, M. Wang, X. Thu, 16:00–18:00, virtuell
Fri, 13:00–14:30, virtuell
Wed, 10:00–12:00, virtuell
Thu, 16:00–18:00, virtuell
WS 2019/20 Techniques in Artificial Intelligence (IN2062) Althoff, M. Klischat, M. Kochdumper, N. Koschi, M. Maierhofer, S. … (total 7) Fri, 13:00–14:30, MW 2001
Thu, 16:00–18:00, MW 0001
Wed, 10:00–12:00, MI 03.13.010
WS 2018/9 Techniques in Artificial Intelligence (IN2062) Althoff, M. Kochdumper, N. Koschi, M. Miller, C. Fri, 13:00–14:30, MW 2001
Wed, 14:00–16:00, MI HS1
Wed, 11:00–12:00, MI 03.07.023
and singular or moved dates
WS 2017/8 Techniques in Artificial Intelligence (IN2062) Althoff, M. Fri, 12:45–14:15, MW 0001
Wed, 14:00–16:00, MI HS1
and singular or moved dates
WS 2016/7 Techniques in Artificial Intelligence (IN2062) Althoff, M. Fri, 12:45–14:15, MW 0001
Wed, 14:00–16:00, MI HS1
WS 2015/6 Techniques in Artificial Intelligence (IN2062) Wed, 14:00–16:00, MI HS1
Fri, 12:00–14:00, Interims I 101
and singular or moved dates
WS 2014/5 Techniques in Artificial Intelligence (IN2062) Thu, 16:00–18:00, Interims I 101
Fri, 12:00–14:00, Interims I 101
and singular or moved dates
WS 2013/4 Techniques in Artificial Intelligence (IN2062) Thu, 16:00–18:00, Interims I 101
Fri, 12:00–14:00, Interims I 101
and singular or moved dates
WS 2012/3 Techniques in Artificial Intelligence (IN2062)
Top of page