Course syllabus - Learning Systems

Objectives

This course aims to give a wide knowledge in different learning systems, advantages, limits and application areas.

Learning outcomes

After completing the course the student should be able to:

1. understand machine learning algorithms,  
2. formulate a problem, apply an algorithm and explain why it is suitable for the given problem as well as show how it can solve the problem and also
3. analyze the results of applying an algorithm to a problem, and discuss the advantages and limitations of the algorithm.

Course content

Linear regression, logistic regression, decision trees, Bayesian learning,  metaheuristic optimization (genetic algorithm, differential evolution), K-means clustering, principal component analysis,  artificial neural networks, reinforcement learning (Q-learning).

Specific requirements

90 credits of completed courses including Programming 7.5 credits and Programming for Embedded Systems 7.5 credits or Data Structures, Algorithms and Program Development 7.5 credits. 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

Written examination (TEN1), 3.5 credits, examines the learning outcomes 1 and 2, marks Fail (U), 3, 4 or 5.
  
Laboratory work  (LAB1), an assignment that is presented with a report and a demonstration to the teacher, 4 credits, examines the learning objectives 1, 2 and 3, marks Fail (U) or Pass (G). 

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 CDT407/DVA427 Learning Systems and DVA308 Maskininlärning.