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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.

    • Ratified date: 2019-10-15
    • Latest update: 2019-10-15

    Books

    Mitchell, Tom M.

    Machine learning

    New York : McGraw-Hill, cop. 1997 - xvii, 414 s.

    ISBN: 0-07-042807-7 LIBRIS-ID: 8273683

    Mandatory

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.

Study guide

Grade

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course overlaps 7.5 credits with CDT407 Learning Systems.