Computer Science, Second Cycle, Machine Learning, 6 credits

Learning outcomes

Knowledge and understanding
Completing this course, the student will know about the fundamental concepts in machine learning, the different classes of machine learning algorithms, and ways to chose and apply different basic machine learning algorithms. Furthermore, the student will learn about ways to evaluate the performance of learning systems.

Applied knowledge and skills
Completing this course, the student will be able to prepare data and apply machine learning methods to achieve a learning goal within an intelligent system.

Making judgments and attitudes
Completing this course, the student will be able to judge the suitability of a machine learning paradigm for a given problem and the available data, have an understanding of the capabilities and limitations of the considered machine learning algorithms, and is able to identify problems or misleading results.

Content

Machine Learning Algorithms

• core concepts and algorithms used for supervised and unsupervised learning,
• k-nearest Neighbors,
• Artificial Neural Networks,
• Decision Trees,
• Ensemble Learning and Boosting,
• Clustering, and
• Evaluation and analysis of the performance of machine learning algorithms.

Applied Machine Learning

• Application of machine learning algorithms for the tasks of classification and prediction,
• Methods for data preprocessing including normalisation, feature extraction, dimensionality reduction, and re-balancing,
• Principal Component Analysis,
• Bias-Variance Dilemma, and
• Practical recommendations.

Examinations and grades

Theory, 2 credits (Code: A001)
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).

Literature Presentations, 2 credits (Code: A002)
Grades used are Fail (U) or Pass (G).

Case-Based Study, 2 credits (Code: A003)
Grades used are Fail (U) or Pass (G).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (U), Pass (G) or Pass with Distinction (VG).

Comments on grades

Final grades are given from test A001, given that tests A002 and A003 are passed.

Modes of assessment

• Theory (code A001): Written assignment
• Literature presentations (code A002): Oral examination
• Case-based study (code A003): Written assignment

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations.

Other provisions

The course is given in English.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

No course literature is required.