Course syllabus - Artificial Intelligence 1
Scope
7.5 credits
Course code
DVA251
Valid from
Autumn semester 2020
Education level
First cycle
Progressive Specialisation
G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements).
Main area(s)
Computer Science
School
School of Innovation, Design and Engineering
Ratified
2020-01-24
Status
This syllabus is not current and will not be given any more
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
Fourth edition global edition : Harlow : Pearson Education Limited, 2022 - 1166 pages
ISBN: 1292401133 LIBRIS-ID: p3vvfhm1m3dsszsg
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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 course aims to lay a foundation for the subject of artificial intelligence through both theory and practice. The course also aims at providing case studies in the related area.
Learning outcomes
After completing the course, the student shall be able to:
1. explain, and apply, the agent abstraction model according to the three-step model, that is (i) perceive the world, (ii) reason about the world, and (iii) manipulate the world through action,
2. define a problem, and know how to decide different representations of the problem, based on states, in order to improve the performance of the algorithm that will solve the problem,
3. apply the most representative uninformed search strategies in a state search space and know its efficiency in time and space,
4. apply the most representative informed search strategies in a state search space,
5. understand what heuristic means and analyze its repercussions on time and space efficiency,
6. understand the meaning of optimization, and well-known search strategies,
7. have an intuition on what feature selection and classification means,
8. use adversarial search techniques and know their relation to games,
9. understand how to solve a constraint satisfaction problem and define the search space for these problems and also
10. analyze and define a given problem, as well as decide if it can be solved with one of the learned techniques.
Course content
- Introduction to artificial intelligence.
- Types of problems, in which AI can be applied and how to define them.
- Search algorithms, such as Breath-first, Depth-first, and A* (other methods may be included based on the context).
- Trajectory-based optimization, such as Hill climbing and simulated annealing (other methods may be included based on the context).
- Adversarial search, such as minimax and alpha-beta pruning (other methods may be included based on the context), and games.
- Constraint satisfaction problems.
Specific requirements
Computer Programming with Python 7.5 credits and also Data Structures, Algorithms and Program Development with Python 7.5 credits or corresponding.
Examination
Laboratory work 1 (LAB1), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 2, 3 and 10, marks Fail (U), 3, 4 or 5.
Laboratory work 2 (LAB2), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 2, 4, 5 and 10, marks Fail (U), 3, 4 or 5.
Laboratory work 3 (LAB3), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 2, 6, 7 and 10, marks Fail (U), 3, 4 or 5.
Laboratory work 4 (LAB4), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 1, 2, 8 and 10, marks Fail (U), 3, 4 or 5.
Laboratory work 5 (LAB5), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 2, 9 and 10, 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 towards CDT312/DVA340 Artificial Intelligence.
The course contributes to fulfill the degree requirement of at least 75 credits in the main area of computer science with the focus of intelligent systems for technology bachelor's degree in computer science with the focus of intelligent systems.