• CM152A Mathematical Statistics (7.5 credits)
• CD102A Object-Oriented Programming (7.5 credits)
• CD120A Algorithms and Data Structures (7.5 credits)

The course is part of the degree requirements for a Master of Science in Engineering in Computer Science and Engineering (specialisation Applied Data Science)

The aim of the course is to familiarise the student with the basic methods and techniques in the field of artificial intelligence and autonomous systems, with particular emphasis on practical use in the development of software for data science problems.

The course includes the following elements:

• Recommendation systems: user- and content-based recommendations, recommendation algorithms (such as neighborhood-based, collaborative filtering and matrix factorisation), context-aware recommendations, cold start, eliciting/implicit ratings, evaluation and metrics.
• Information retrieval, knowledge acquisition, knowledge representation and reasoning, the semantic web, constructing and querying knowledge graphs, extracting data from online sources and source alignment
• Probabilistic models and decision theory, decision making under uncertainty, optimisation, dynamic programming, methods for adversarial and heuristic search
• Practical methods for data mining
• Machine learning for both supervised and unsupervised learning. Algorithms for classification, prediction, and clustering.

Knowledge and understanding

Upon completion of the course, the student shall be able to:

1. explain the basic concepts and methods based on, among other things, logic and neural networks, which are used when applying AI to data science.

Competence and skills

Upon completion of the course, the student shall be able to:

2. use knowledge about AI and the basic concepts in the field together with the applicable principles and guidelines to put together solutions to exercises in AI,

3. implement AI-based solution methods, individually as well as in groups, and

4. communicate clearly and effectively using technical terminology applicable to the area.

Judgement and approach

Upon completion of the course, the student shall be able to:

5. evaluate different methods for extracting and processing information from large amounts of data, based on underlying theory as well as practical effect, and

6. evaluate and compare the suitability of different AI methods for a given problem.

Lectures, data laboratories, seminars and self-study.

The following are required to pass the course

• passing grade on written assignments (7.5 credits, Pass/Fail) (Intended learning outcomes 2–4, 6)
• passing grade on written examination (7.5 credits, U–A) (intended learning outcomes 1, 2, 5)

For all assessments, the materials must be presented in a manner that makes it possible to discern individual performance.

The final grade corresponds to the grade of the written examination.

• Han, J., Kamber, M., & Pei, J. (2022). Data mining: Concepts and techniques (4th edition). Morgan Kaufmann.
• Russell, S. J. & Norvig, P. (2020). Artificial intelligence: a modern approach. (4th edition), Pearson.
• Segaran, T. (2007). Programming Collective Intelligence, O'Reilly.

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented. The syllabus is a translation of a Swedish source text.

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format. The syllabus is a translation of a Swedish source text.