Print Course syllabus
Course syllabus - Artificial Intelligence
Scope
7.5 credits
Course code
DVA340
Valid from
Autumn semester 2024
Education level
First cycle
Progressive Specialisation
G2F (First cycle, has at least 60 credits in first-cycle course/s as entry requirements).
Main area(s)
Computer Science
School
School of Innovation, Design and Engineering
Ratified
2018-02-01
Revised
2024-01-18
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
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Books
Artificial intelligence : a modern approach
3.,[updated] ed. : Boston : Pearson Education, cop. 2010 - xviii, 1132 s.
ISBN: 9780132071482 (pbk.) LIBRIS-ID: 11712972
Objectives
The aim of this course is to cover theories as well as techniques that are fundamental to artificial intelligence.
Learning outcomes
After completing the course, the student shall be able to:
1. describe mile stones of AI and relate them to computer science as well as other fields
2. implement software that can use most common AI-problems
3. define the size and characteristics of a search space for a given problem and suggest suitable AI algorithm and representation 4. successfully apply AI algorithms to problem solving
Course content
- Types of problems, in which AI can be applied and how to apply an AI method to them
- Search algorithms: breath-first, depth-first, iterative deepening, informed search, Greedy-Best-first and A*
- Optimization algorithms: Genetic Algorithms, Differential Evolution, Particle Swarm Optimization and Ant Colony Optimization
- Classification method: Artificial Neural Network
Tuition
Lectures and laboratory work.
Specific requirements
Data Structures and Algorithms 7.5 credits, Object Oriented Programming 7.5 credits and an additional fulfilled course in computer science at 7.5 credits that emphasise programming skills, for example Operating Systems or Functional Programming.
Examination
Laboratory work (LAB1), 1,5 credits, examines the learning objectives 1-4, marks Fail (U), 3, 4 or 5
Laboratory work (LAB2), 1,5 credits, examines the learning objectives 1-4, marks Fail (U), 3, 4 or 5
Laboratory work (LAB3), 1,5 credits, examines the learning objectives 1-4, marks Fail (U), 3, 4 or 5
Laboratory work (LAB4), 1,5 credits, examines the learning objectives 1-4, marks Fail (U), 3, 4 or 5
Laboratory work (LAB5), 1,5 credits, examines the learning objectives 1-4, marks Fail (U), 3, 4 or 5
A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.
Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.
Grade
Pass with distinction, Pass with credit, Pass, Fail
Interim Regulations and Other Regulations
The course completely overlaps with CDT312 Artificiell intelligens, DVA251/DVA264 Artificiell intelligens 1 and DVA255/DVA265 Artificiell intelligens 2.