TUM course outline

Name: Practical Course - Algorithms for Programming Contests (IN0012, IN2106, IN4205)
Code: 0000001435
ECTS: 10
Link: https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950272197
Link: https://www7.in.tum.de/um/courses/praktika/conpra/WS16/index.php?category=content
Description: Goals of this course are a deep understanding of fundamental algorithms and data structures, getting to know specialized algorithms used in current research, improved skills in problem solving and problem analysis, practice in recognizing needed algorithms for given problems on one's own, improved capacities in teamwork by taking part in team contests, the application of important mathematical techniques as well as preparing the participants for programming contests.
Equivalent ANU course: COMP3600 (Algorithms)

Name: Game Physics (IN0037)
Code: 0000000370
ECTS: 6
Link: https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950266754&pSpracheNr=2&pMUISuche=FALSE
Link: http://wwwcg.in.tum.de/teaching/teaching/winter-term-1617/game-physics.html
Description: 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.
Equivalent ANU course: Elective

Name: Computational Intelligence
Code: 0000001859
ECTS: 7
Link: https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950265829&pSpracheNr=2&pMUISuche=FALSE
Link: http://ci.nst.ei.tum.de/
Description: Introduction to theory and application of neuronal networks, fuzzy control techniques, search- and exploration-based machine learning approaches for optimization, support-vector machines, statistical learning methods, evolutionary and genetic algorithms for optimization, reinforcement learning, distributed agent-based learning.
Equivalent ANU course: COMP3620 (Artificial Intelligence)

Name: Techniques in Artificial Intelligence (IN2062)
Code: 240927786
ECTS: 5
Link: https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950264119&pSpracheNr=2&pMUISuche=FALSE
Description:
- You can analyze problems of artificial intelligence and judge how difficult it is to solve them.
- You can recall the basic concepts of intelligent agents and know possible task environments.
- You can formalize, apply, and understand search problems.
- You understand the difference between constraint satisfaction and classical search problems as well as apply and evaluate various constraint satisfaction approaches.
- You can critically assess the advantages and disadvantages of logics in artificial intelligence.
- You can formalize problems using propositional and first-order logic.
- You can apply automatic reasoning techniques in propositional and first-order logic.
- You understand the advantages and disadvantages of probabilistic and logic-based reasoning.
- You can apply and critically asses methods for probabilistic reasoning with Bayesian networks and Hidden Markov Models.
- You understand and know how to compute rational decisions.
- You have a basic understanding on how a machine learns.
- You know the basic areas and concepts in robotics.
Equivalent ANU course:

Name: Microprocessors (IN2075)
Code: 0000001509
ECTS: 5
Link: https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950263674&pSpracheNr=2&pMUISuche=FALSE
Link: https://www.lrr.in.tum.de/lehre/wintersemester-1516/vorlesungen/microprocessors/
Students are taught both detailed hardware knowledge as well as hardware related software aspects (e.g. operating system support), with an emphasis on contemporary microprocessors. After participating in this lecture, students are capable of choosing an optimal processor architecture from the numerous general and special purpose approaches for a given problem. This also applies to hardware related programming issues.
Equivalent ANU course: