Course syllabus - Learning Systems
Scope
7.5 credits
Course code
DVA427
Valid from
Autumn semester 2019
Education level
Second cycle
Progressive Specialisation
A1N (Second cycle, has only first-cycle course/s as entry requirements).
Main area(s)
Computer Science
School
School of Innovation, Design and Engineering
Ratified
2013-01-29
Revised
2019-01-25
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
Machine learning
New York : McGraw-Hill, cop. 1997 - xvii, 414 s.
ISBN: 0-07-042807-7 LIBRIS-ID: 8273683
Objectives
This course aims to give a wide knowledge in different learning systems, advantages, limits and application areas.
Learning outcomes
Upon completion of the course the student should be able to:
1. having a (vague) problem definition choose an algorithm and explain why it is suitable for the given problem as well as show how it can solve the problem
2. analyze the results of applying an algorithm to a problem, and discuss the results and propose improvements or other more suitable algorithms
3. write a scientific report and explain every step from definition of a problem to analysis of the results
Course content
Artificial neural nets; evolutionary algorithms; reinforcement learning; Bayesian nets; case-based reasoning; clustring and fuzzy systems.
Specific requirements
90 credits including Programming 7.5 credits and Data Structures, Algorithms and Program Development 7.5 credits, or equivalent. In addition Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.
Examination
Exercise (INL1), 1.5 credits, marks Pass (G) (examines learning outcome 3)
Laboratory work (LAB1), 3 credits, marks Pass (G) (examines learning outcomes 1-2)
Examination (TEN1), 3 credits, marks 3, 4 or 5 (examines learning outcomes 1-2)
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 overlaps 7.5 credits with CDT407 Learning Systems.