• Bachelor of Science in computer science or related subjects.
• Knowledge equivalent to English 6 at Swedish upper secondary level.
• At least 15 credits in programming.
• At least 7.5 credits in mathematics.

The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).

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
For a passing grade the student shall be able to:

• Explain the basic concepts and methods of the AI field

Competence and abilities
For a passing grade the student shall be able to:

• 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.
• Implement AI-based solution methods, individually as well as in groups.
• Communicate clearly and effectively using technical terminology applicable to the area

Evaluation abilities and approach
For a passing grade the student shall be able to:

• Evaluate different methods for extracting and processing information from large amounts of data, based on underlying theory as well as practical effect
• Evaluate and compare the suitability of different AI methods for a given problem

Lectures, data laboratories, seminars

The students are assessed with a written exam (7,5 credits, A-E) and written assignments (7,5 credits, pass/fail)

For a pass, the student needs to pass a written examination (7.5 credits) and written assignments (7.5 credits).

The final grade is based on the examination.

• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham: Morgan Kaufmann.
• Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. (3rd ed.) Boston: Pearson Education.
• Segaran, Toby. 2007. Programming Collective Intelligence (First ed.). O'Reilly.

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).

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

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.