Artificial intelligence for data science

Summary

The purpose of the course is that the student acquires 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.

Admission requirements

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

Selection:

credits 100%

Syllabus

Syllabus for students autumn 2020

Course Code:
DA631E revision 1
Swedish name:
Artificiell intelligens för data science
Level of specialisation
A1N
Main fields of study:
Computer Science
Language:
English
Date of ratification:
25 March 2019
Decision-making body:
Faculty of Technology and Society
Enforcement date:
31 August 2020

Entry requirements

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

Specialisation and progression relative to the degree regulations

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).

Purpose

The purpose of the course is that the student acquires 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.

Contents

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.

Learning outcomes

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

Learning activities

Lectures, data laboratories, seminars

Assessments

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.

Grading system

Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U).

Course literature and other teaching materials

  • 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.
  • Toby Segaran. 2007. Programming Collective Intelligence (First ed.). O'Reilly.

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).

Interim rules

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.

Other Information

The syllabus is a translation of a Swedish source text.

Contact

The education is provided by the Faculty of Technology and Society at the Department of Computer Science and Media Technology.

Further information

Application

31 August 2020 - 17 January 2021 Day-time 50% Malmö This course is offered as part of a program

Tuition fees

for non-EU students only

First instalment: 32000 SEK
Full tuition Fee: 32000 SEK

31 August 2020 - 17 January 2021 Day-time 50% Malmö Application code: mau-08438

National application round

Tuition fees

for non-EU students only

First instalment: 32000 SEK
Full tuition Fee: 32000 SEK

Application deadline 15 April

Apply