Swedish name: Masterprogrammet i artificiell intelligens
This programme syllabus is valid: HT21 and valid until HT22 (newer version of the programme syllabus exists)
Programme syllabus for programmes starting before HT22
Programme code: TAAIM
Credit points: 120
Registration number: FS 3.1.3-360-19
Responsible faculty: Faculty of Science and Technology
Established by: Faculty Board of Science and Technology, 2019-07-02
Revised by: Faculty Board of Science and Technology, 2021-03-02
A Bachelor's degree or equivalent first-cycle qualification comprising of at least 180 ECTS or a corresponding qualification from an internationally recognised university. Specific entry requirements are at least 90 ECTS in the field of Computing Science, Cognitive Science, Mathematics or Mathematical Statistics, or equivalent. Of these, at least 30 ECTS must be in the subject of Computing Science and include courses in Programming Methodology, Data Structures and Algorithms, and at least 22,5 ECTS in the subject of Mathematics including courses in Calculus, Linear Algebra and one course in either Logic or in Statistics. Proficiency in English equivalent to Swedish upper secondary course English B/6.
After completing the programme comprising 60 credits, a student who has applied for a degree can obtain a Degree of Master of Science (60 credits) in Computer Science, either general or with a specialisation in Artificial Intelligence.
After completing the programme comprising 120 credits, a student who has applied for a degree can obtain a Degree of Master of Science (120 credits) in Computer Science, either general or with a specialisation in Artificial Intelligence, or a Degree of Master of Science (120 credits) in Mathematical Statistics.
All qualifications above shall be obtained in accordance with local qualification descriptor established by Vice-Chancellor, see https://www.umu.se/student/mina-studier/examen/krav-och-huvudomraden/examensbeskrivningar/
In Swedish, Degree of Master of Science (60 credits) is called Teknologie magisterexamen, and Degree of Master of Science (120 credits) is called Teknologie masterexamen. In Swedish, Degree of Master of Science (120 credits) is called Filosofie masterexamen. Degree is awarded in one of the main fields of study, Computer Science or Mathematical Statistics. In Computer Science, you can obtain either a general qualification or with the specialisation Artificial Intelligence.
The education is at an advanced level (second cycle). The aims for second-cycle courses and study programmes are set out in the Higher Education Act, Chapter 1 Section 9.
Second-cycle courses and study programmes shall involve a deepening of knowledge, skills and abilities relative to first-cycle studies and, in addition to what applies to first-cycle studies, shall
The national aims for qualification are set out in the Higher Education Ordinance's Annex 2.
Degree of Master (60 credits)
Knowledge and understanding
For a Degree of Master (60 credits) the student shall
Degree of Master (60 credits)
For Master of Science in Computer Science with a specialisation in Artificial Intelligence, the student shall be able to:
The courses in the programme have a great variation in both teaching and examination formats. Instead of summative assessments, we focus on more formative assessments, where students participate and influence how they are carried out and student active elements such as seminars and projects. Each syllabus sets out the examination formats used in each individual course.
Each syllabus sets out the grades used in the course.
A student who believes to have gained knowledge from previous relevant studies or professional experience that may be equivalent to a course or part of a course in the programme can apply for transfer of credits. Granting a transfer of credits means that the student will not have to study the parts of the programme included in the decision. Information on transfer of credits is available on Umeå University's website.
https://www.umu.se/en/student/my-studies/transfer-of-credits/
The degree programme includes a total of 120 credits, of which 30 credits comprise an independent degree project. The programme includes compulsory courses, elective courses and free electives. During the first part of the programme, the compulsory courses provide a common knowledge base in Artificial Intelligence. Many courses consist of laboratory work, where the student can work with problems related to AI, often in collaboration with industry or public activities. The education is completed with a degree project during term four.
The programme leads to several possible qualifications. If you are aiming for a Degree of Master of Science (120 credits) in Computer Science with a specialisation in Artificial Intelligence, it is possible to profile in four subareas within the area of Artificial Intelligence: Reasoning and decision-making (Reasoning), Machine learning (Learning), Human-AI interaction (Interaction) and Intelligent Robotics. Provided that the entry requirements regarding courses in Computer Science are met, you can instead choose to obtain a degree in Computer Science without specialisation in Artificial Intelligence. If you are aiming for a Degree of Master (120 credits) in Mathematical Statistics, you take courses in the subarea Data Science. Please note that it is not possible to obtain a Degree of Master (60 credits) in Mathematical Statistics. Elective courses can be selected provided that the entry requirements for each respective course are met and that an equivalent course was not included in Degree of Bachelor.
The courses included in the programme are listed under the heading 'Study Plan' in the order they are studied. The order of the courses are, however, subject to change. Selection of elective courses/free electives is made in consultation with programme coordinator. Information on the layout of individual courses is available in the different course syllabi.
Compulsory courses for both main fields of study (Computer Science and Mathematical Statistics)
Compulsory courses are courses that all students enrolled in the programme normally study. A student enrolled in the study programme is guaranteed a seat in all compulsory courses, provided that the entry requirements for the course in question are met. Entry requirements are listed in each respective course syllabus. The courses also provide basic knowledge for the specialisation profiles of the programme. All courses listed in this programme syllabus are at second-cycle level unless stated otherwise.
Information on deferment of studies is available on Umeå University's website.
Information on approved leave from studies is available on Umeå University's website.
Information on discontinuation is available on Umeå University's website.
Admission to the courses in the programme is regulated in the course syllabi. The degree shall, in addition to the independent work, include courses in accordance with the requirements listed in the qualification descriptor.
Study plan valid from: Autumn 2020
Established: 2019-07-02
Established by: Faculty Board of Science and Technology
Table 1. Overview of possible specialisations for a one-year programme with Degree of Master (60 credits) in Computer Science with a specialisation in Artificial Intelligence. Courses in italics are examples of elective courses, other courses are compulsory.
Year 1 | Degree of Master (60 credits) in Computer Science with a specialisation in Artificial Intelligence |
LP1 | 5DV102 Fundamentals of Logic and Model Theory, 7.5 credits or 5MS069 Statistics for Engineers, 7.5 credits |
5DV121 Fundamentals of Artificial Intelligence, 7.5 credits | |
LP2 | 5DV185 Interactivity in smart environments, 7.5 credits |
5DV181 Artificial Intelligence - Methods and Applications, 7.5 credits | |
LP3 | 5DV194 Machine learning, 7.5 credits |
5DV211 Responsible Design of Interactive AI-Systems, 7.5 credits | |
LP4 | 5DV215 Degree Project for a Degree of Master (60 credits) in Computer Science with a specialisation in Artificial Intelligence, 15 credits |
Year 1 | Human-AI interaction | Reasoning and decision-making | Intelligent Robotics | Machine learning | Data Science |
LP1 | 5DV102 Foundations of Logic and Model Theory, 7.5 credits or 5MS069 Statistics for Engineers, 7.5 credits | ||||
5DV121 Fundamentals of Artificial Intelligence, 7.5 credits | |||||
LP2 | 5DV184 Interactivity in smart environments, 7.5 credits | 5MS049 Stochastic Processes and Simulation, 7.5 credits | |||
5DV181 Artificial Intelligence - Methods and Applications, 7.5 credits | |||||
LP3 | 5DV194 Machine learning, 7.5 credits | ||||
5DV211 Responsible Design of Interactive AI-Systems, 7.5 credits | 5DV217 Data Processing and Visualisation, 7.5 credits | ||||
LP4 | 5DV210 Trends in interactive intelligent environments, 7.5 credits | 5DV190 Project course in Machine Vision, 7.5 credits | 5DV210 Trends in interactive intelligent environments, 7.5 credits | 5MS071 Design of Experiments and Advanced Statistical Modelling, 15 credits | |
5DV183 Human Robot Interaction, 7.5 credits | Elective | 5DV183 Human Robot Interaction, 7.5 credits | Elective | ||
Year 2 | |||||
LP1 | 5DV188 Cognitive Interaction Design, 7.5 credits | 5DVXXX Reasoning and decision-making, 7.5 credits | 5DV218 Natural Language Processing, 7.5 credits | 5MS056 Multivariate Data Analysis,7.5 credits | |
Elective | Elective | Elective | Elective | Elective | |
LP2 | 5DV219 Individual project in Artificial Intelligence, 7.5 credits | 5MS062 Big Data and high-dimensional data analysis, 7.5 credits | |||
Elective | Elective | Elective | Elective | Elective | |
LP3 LP4 | 5DV216 Degree Project for a Degree of Master (120 credits) in Computer Science with a specialisation in Artificial Intelligence, 30 credits | 5MS066 Thesis Project for the Degree of Master of Science in Mathematical Statistics, 30 credits |