TUM 16/17 Winter session

https://campus.tum.de/tumonline/wbLVAngebot.wbShowLVOffer?pOrgNr=14189&pPersonNr=&pSjNr=1603&pGroup=K&pStpLvTypNr=-1&pSort=6&pFilter=null%3Ff_4_1%3DS

Game Physics (IN0037) - 6 ECTS
https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950266754&pSpracheNr=2&pMUISuche=FALSE
Participants acquire knowledge of the physical simulation of real phenomena, and effects which are increasingly prevalent in computer games. Numerous physical models are discussed, and corresponding numerical approaches for solving them are demonstrated. The focus of these methods are interactive environments for real-time applications.
An additional goal of the curriculum is the ability to implement simulation algorithms and integration methods with the C++ programming language.

Fundamental Algorithms (CSE) (IN2157) - 3 ECTS
https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950263770&pSpracheNr=2&pMUISuche=FALSE
The course will provide an overview on the analysis of fundamental algorithms. Topics will be:
Fundamentals: Models of Computation, Complexity Measures
Sorting: Bubble-Sort, Merge-Sort, Quick-Sort, Median-Algorithms, Lower Bounds, etc.: sorting in parallel
Searching: Hashing, Search Tress, etc.
Arithmetic Problems: parallel prefix computation, parallel matrix and vector operations
Foundations of parallel algorithms and simple models of parallel computation
Algorithms on (weighted) graphs: traversals, shortest paths, etc.

Microprocessors (IN2075) - 5 ECTS
https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950263674&pSpracheNr=2&pMUISuche=FALSE

Techniques in Artificial Intelligence (IN2062) - 5 ECTS
https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950264119&pSpracheNr=2&pMUISuche=FALSE
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.