Print Course syllabus
Course syllabus - Machine Learning
Scope
7.5 credits
Course code
DVA308
Valid from
Autumn semester 2022
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
2022-01-24
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
Machine learning
New York : McGraw-Hill, cop. 1997 - xvii, 414 s.
ISBN: 0-07-042807-7 LIBRIS-ID: 8273683
Objectives
This course aims to provide an in depth overview on learning systems within the topic of artificial intelligence, their advantages as well as disadvantages in the context of various specific problems, and in general, their limitations 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
- Metaheuristic optimization: Genetic Algorithms, Differential Evolution.
- Supervised Learning: Artificial Neural Network, Bayesian learning, Decision Tree.
- Unsupervised Learning: K-means, PCA.
- Reinforcement Learning: Q-Learning.
Specific requirements
Completed courses of 75 credits in computer science, of which includes Programming 7,5 credits, Data Structures, Algorithms and Program Development 7,5 credits, Artificial Intelligence 7,5 credits and Artificial Intelligence 2 7,5 credits or equivalent. In addition, Basic vector algebra 7,5 credits, Basic calculus 7,5 credits and Probability theory and statistical inference 7,5 credits are needed.
Examination
Written examination (TEN1), 3,5 credits, examines the learning outcomes 1 and 2, marks 3, 4 or 5.
Laboratory work 1 (LAB1), An assignment that is presented with a report and a demonstration to the teacher, 4 credits, examines the learning outcomes 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 DVA493/DVA427/CDT407 Learning systems.
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.