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Kursusbeskrivelse
Kursusnavn (dansk):Intelligent Systems Programming, MSc 
Kursusnavn (engelsk):Intelligent Systems Programming, MSc 
Semester:Forår 2018 
Udbydes under:cand.it., softwareudvikling og -teknologi (sdt) 
Omfang i ECTS:7,50 
Kursussprog:Engelsk 
Kursushjemmeside:https://learnit.itu.dk 
Min. antal deltagere:
Forventet antal deltagere:
Maks. antal deltagere:145 
Formelle forudsætninger:*You must have passed an elementary programming course. (for example
Introductory Programming)
*You must have passed a discrete mathematics course (for example Foundations of Computing, Discrete Mathematics)
*You must follow in parallel, or have passed an introductory algorithms course (for example Foundations of Computing, Algorithms and Data Structures).

Notice that these courses are NOT a formal requirement for GAMES students.

If you are an external student, it is important that you have programming-experience from elsewhere, i.e. through a daily use in a developer position in the software industry.

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Information about the course of study
This course is part of a specialization on the Master of Science in IT, study programme Software Development and Technology.
This course is first part of a specialization on the Master of Science in IT, study programme GAMES. 
Læringsmål:After the course, the student should be able to:
* Identify decision problems in work processes and IT products that can
be solved by AI and optimization algorithms.
* Apply advanced AI and optimization modeling techniques to describe
these problems formally.
* Implement AI and optimization software components to solve
these problems efficiently.
* Apply standard AI and optimization models and solvers.
* Participate in concept development of advanced decision
support systems. 
Fagligt indhold:The overall goal of the course is to introduce students to a selection of the most important problem solving and decision support techniques within AI and optimization. The goal is to make students able to identify, design, and implement efficient solutions to the kind of decision problems that arise in modern organizations and IT products. The expectation is that a student mastering the material is able to work in internationally leveled business intelligence and optimization groups as well as in development departments of “intelligent” applications as used in smart phones, computer games, enterprise resource planning systems, and decisions support systems.

The course will cover the followings topics:

Search algorithms
* Informed search: greedy heuristic search, A*, breadth-first heuristic search
* Local search: hill-climbing, simulated annealing, genetic algorithms, tabu search, population-based search
* Adversarial search: Minimax search, alpha-beta pruning

Propositional logic
* Representations: disjunctive (DNF), conjunctive (CNF), and if-then-else (INF) normal forms, Binary Decision Diagrams (BDDs)
* Reasoning: resolution, SAT-checking

Constraint programming
* Local consistency: arc-consistency, path-consistency, i-consistency
* Look-ahead search strategies: forward-checking, arc-consistency look-ahead, maintaining arc-consistency

Linear Programming
* Simplex algorithm
* Duality

The course provides useful tools in its own right, but it is also the first course on the modern AI specialization and the scalable computing specialization. 
Læringsaktiviteter:14 ugers undervisning bestående af forelæsninger og øvelser

13 lectures + 11 exercise sessions.

The overall goal of the course is to introduce students to a selection of the most important problem solving and decision support techniques within AI and optimization. The goal is to make students able to identify, design, and implement efficient solutions to the kind of decision problems that arise in modern organizations and IT products. The expectation is that a student mastering the material is able to work in internationally leveled business intelligence and optimization groups as well as in development departments of “intelligent” applications as used in smart phones, computer games, enterprise resource planning systems, and decisions support systems.

The course will cover the followings topics:

Search algorithms
* Informed search: greedy heuristic search, A*, breadth-first heuristic search
* Local search: hill-climbing, simulated annealing, genetic algorithms, tabu search, population-based search
* Adversarial search: Minimax search, alpha-beta pruning

Propositional logic
* Representations: disjunctive (DNF), conjunctive (CNF), and if-then-else (INF) normal forms, Binary Decision Diagrams (BDDs)
* Reasoning: resolution, SAT-checking

Constraint programming
* Local consistency: arc-consistency, path-consistency, i-consistency
* Look-ahead search strategies: forward-checking, arc-consistency look-ahead, maintaining arc-consistency

Linear Programming
* Simplex algorithm
* Duality

The course provides useful tools in its own right, but it is also the first course on the modern AI specialization and the scalable computing specialization. 

Obligatoriske aktivititer:• Mandatory Exercises. You must hand-in and pass 3 out of 10 exercises to qualify for taking the exam. You can start working on a mandatory problem, as soon as the exercise each week is posted in LearnIT. This will at the latest happen on the day of the recitation of the exercise. You hand in a mandatory problem at the next recitation session. This means that you have at least one week to work on the exercise. You can work on mandatory problems in groups, but they must be handed in individually. The teaching assistants will spend one week evaluating the exercises and they will be handed back at the next recitation session. Students who do not pass 3 exercises will get a possibility to turn in revised solutions before the exam. Details will be available in LearnIT.

• Mandatory projects. During the semester there will be 3 implementation projects. You must hand-in and pass 2 of these to qualify for taking the exam. The projects are made in groups of 2-3 students. You approximately 3 weeks to finish each project. Students who do not pass 2 projects will get a possibility to turn in revised solutions before the exam. Details will be available in LearnIT.

Be aware: The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt 
Eksamensform og -beskrivelse:A11: Skriftlig eksamen (stedprøve) med adgang til internet, skriftlige og trykte hjælpemidler., (7-scale, external exam)

The student is allowed to use all aids: books, notes, software, on-line resources
The duration of the exam is 4 hours.  

Litteratur udover forskningsartikler:1) Russel, S and Norvig, P., "Artificial Intelligence: A Modern Approach", Third Edition, Prentice Hall, 2010, ISBN-13 978-0132071482

2) Notes 
 
Undervisere
Følgende personer underviser på kurset:
NavnStillingUndervisertypeIndsats (%)
Rune Møller Jensen Lektor(ITU) Kursusansvarlig 85
Mai Lise Ajspur Postdoc(ITU) Underviser 15
Tobias Tuxen Hjælpelærer(ITU) Hjælpelærer 0
Daniela Rocha E Andrade Hjælpelærer(ITU) Hjælpelærer 0
Jacob Mathias Schreiner Hjælpelærer(ITU) Hjælpelærer 0