About the course

The purpose of the course is for the student to acquire in-depth knowledge and understanding of advanced aspects of machine learning and familiarise him or herself with recent developments in this field.

This course is offered as part of a programme:

Computer Science: Applied Data Science, Master's Program (Two-Year)

Course content

Syllabus spring 2024

Earlier syllabuses

Entry requirements and selection

Entry requirements

  1. Bachelor of Science in computer science or related subjects.
  2. At least 15 credits in programming.
  3. At least 7.5 credits in mathematics.
  4. Knowledge equivalent to English 6 at Swedish upper secondary level
  5. Passing grade in the course Statistical methods for Data Science

Participation in the course also requires knowledge obtained from the course Artificial intelligence for Data Science.

Selection

University credits completed 100%

Course literature

  • Aurlien Gron. 2017. Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems (1st ed.). O'Reilly Media, Inc.
  • Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham: Morgan Kaufmann.
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
  • Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. (3rd ed.) Boston: Pearson Education.
  • Witten, Ian H., Frank, Eibe & Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques (3rd ed.) Burlington, MA: Morgan Kaufmann.

• A collection of scientific articles will bed added to the above mentioned literature.

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

Malmö Day-time 100%

Contact

For more information about the education:

tsstudent@mau.se

This education is provided by

Faculty of Technology and Society

The Faculty of Technology and Society (TS), conducts education and research with a focus on technology and natural sciences, in...

Department of Computer Science and Media Technology

The Department of Computer Science and Media Technology provides research-linked courses and study programmes at first-cycle an...