Course syllabus

Computer Science, Machine Learning, Second Cycle, 7.5 credits

Course code: DT101A Credits: 7.5
Main field of study: Computer Science Progression: A1N
    Last revised: 12/09/2019
Education cycle: Second cycle Approved by: Head of school
Established: 30/11/2016 Reading list approved: 12/09/2019
Valid from: Spring semester 2020 Revision: 1

Aims and objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall

  • further develop the ability of students to integrate and make autonomous use of their knowledge
  • develop the students' ability to deal with complex phenomena, issues and situations, and
  • develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course objectives

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.

Main content of the course

Core concepts and algorithms used for supervised and unsupervised learning.
- Application of machine learning algorithms for the tasks of classification, prediction, and clustering.
- Methods for data preprocessing including normalisation, feature extraction and selection, and dimensionality reduction.
- Practical recommendations for applying machine learning algorithms.
- Evaluation and analysis of the performance of machine learning algorithms.

Teaching methods

Teaching methods used in this course are lectures, labs, and seminars. Both the theory and practical exercises are performed individually. All teaching is 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.

Examination methods

Laboratory Work, 4.5 credits (Code: A001)
Written lab reports.

Theory, 3 credits (Code: A002)
Oral seminar presentations.


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

For further information, see the university's local examination regulations (in Swedish).

Grades

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 prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

According to regulations on grading systems for first- and second-cycle education (vice-chancellor's decision 2019-01-15, ORU 2019/00107), one of the following grades is to be used: fail, pass, or pass with distinction. The vice-chancellor or a person appointed by the vice-chancellor may decide on exceptions from this provision for a specific course, if there are special reasons.

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

Laboratory Work
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).

Theory
Grades used are Fail (U) or Pass (G).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

To receive the grade VG for the entire course, the student must have VG on all written lab reports.

Specific entry requirements

First-cycle degree of 180 credits, with Computer Science as the main field of study, and at least 15 credits in mathematics (analysis and algebra). The applicant must also have qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

OR

First-cycle degree of 180 credits, and at least 30 credits in mathematics (analysis and algebra), as well as at least 15 credits in Computer Science or Informatics (which includes programming). The applicant must also have qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Reading list and other teaching materials

Additional Reading

Bishop, Christopher M. (2007)
Pattern Recognition and Machine Learning
Springer


Mitchell, Tom M. (1997)
Machine Learning
McGraw-Hill