Master's Programme in Artificial Intelligence, 120 Credits

Entry Requirements

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.

Degree

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.

Description of the education for current education cycle

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

• further develop the students' ability to independently integrate and use knowledge
• develop the students' ability to deal with complex phenomena, issues and situations, and
• develop the students' potential for professional activities that demand considerable independence or for research and development work

National goals for current degree

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

• demonstrate knowledge and understanding in the main field of study, including both an overview of the field and specialised knowledge in certain areas of the field as well as insight into current research and development work, and
• demonstrate specialised methodological knowledge in the main field of study.

 
Competence and skills
 
For a Degree of Master (60 credits) the student shall

• demonstrate the ability to integrate knowledge and analyse, assess and deal with complex phenomena, issues and situations even with limited information
• demonstrate the ability to identify and formulate issues autonomously as well as to plan and, using appropriate methods, undertake advanced tasks within predetermined time frames
• demonstrate the ability in speech and writing to report clearly and discuss his or her conclusions and the knowledge and arguments on which they are based in dialogue with different audiences, and
• demonstrate the skills required for participation in research and development work or employment in some other qualified capacity.

 
Judgement and approach
 
For a Degree of Master (60 credits) the student shall

• demonstrate the ability to make assessments in the main field of study informed by relevant disciplinary, social and ethical issues and also to demonstrate awareness of ethical aspects of research and development work
• demonstrate insight into the possibilities and limitations of research, its role in society and the responsibility of the individual for how it is used, and
• demonstrate the ability to identify the personal need for further knowledge and take responsibility for his or her ongoing learning.

Degree of Master (120 credits)

Knowledge and understanding
 
For a Degree of Master (120 credits) the student shall

• demonstrate knowledge and understanding in the main field of study, including both broad knowledge of the field and a considerable degree of specialised knowledge in certain areas of the field as well as insight into current research and development work, and
• demonstrate specialised methodological knowledge in the main field of study.

 
Competence and skills
 
For a Degree of Master (120 credits) the student shall

• demonstrate the ability to critically and systematically integrate knowledge and analyse, assess and deal with complex phenomena, issues and situations even with limited information,
• demonstrate the ability to identify and formulate issues critically, autonomously and creatively as well as to plan and, using appropriate methods, undertake advanced tasks within predetermined time frames and so contribute to the formation of knowledge as well as the ability to evaluate this work,
• demonstrate the ability in speech and writing both nationally and internationally to clearly report and discuss his or her conclusions and the knowledge and arguments on which they are based in dialogue with different audiences, and
• demonstrate the skills required for participation in research and development work or autonomous employment in some other qualified capacity.

 
Judgement and approach
 
For a Degree of Master (120 credits) the student shall

• demonstrate the ability to make assessments in the main field of study informed by relevant disciplinary, social and ethical issues and also to demonstrate awareness of ethical aspects of research and development work,
• demonstrate insight into the possibilities and limitations of research, its role in society and the responsibility of the individual for how it is used, and
• demonstrate the ability to identify the personal need for further knowledge and take responsibility for his or her ongoing learning.

Local goals for current degree

Degree of Master (60 credits)
For Master of Science in Computer Science with a specialisation in Artificial Intelligence, the student shall be able to:

• absorb new research results and participate in development work within Artificial Intelligence,
• on an abstract level understand a broad scope of problems in Artificial Intelligence, and how these are related,
• understand, explain and use the theories, methods and practical skills required to creatively contribute to solving problems within Artificial Intelligence based on a practical application,
• create software and design of information systems for artificial intelligence,
• cooperate in interdisciplinary teams, and
• understand, explain how, and participate in ensuring that the integration of Artificial Intelligence in society's activities is carried out with respect to economic and legal aspects in an ethical and responsible way.

Degree of Master (120 credits)
For a Degree of Master of Science (120 credits) in Computer Science with a specialisation in Artificial Intelligence, the student shall be able to:

• absorb new research results and participate in development work within Artificial Intelligence,
• on an abstract level understand and formulate a broad scope of research issues in Artificial Intelligence, and how these are related,
• understand, explain and use the theories, methods and practical proficiencies required to creatively contribute to solving research issues in Artificial Intelligence based on a practical application,
• create innovative software and design of information systems for artificial intelligence,
• cooperate in interdisciplinary teams, and
• understand, explain how, and participate in ensuring that the integration of Artificial Intelligence in society's activities is carried out with respect to economic and legal aspects in an ethical and responsible way.

Examination format

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.

Grades

Each syllabus sets out the grades used in the course.

Transfer of Credits

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/

General

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.

• Foundations of Logic and Model Theory 7.5 credits or Statistics for Engineers, 7.5 credits (the student takes Logic if Statistics is included in their Bachelor and Statistics if Logic is included in their Bachelor). These are first-cycle courses.
• Fundamentals of Artificial Intelligence, 7.5 credits
• Artificial Intelligence - Methods and Applications, 7.5 credits
• Machine learning, 7.5 credits

Compulsory courses for Computer Science
If you are aiming for a degree in Computer Science, you shall, in addition to the courses above, also study the following courses.

• Responsible Design of Interactive AI-Systems, 7.5 credits
• Degree Project for a Degree of Master (120 credits) in Computer Science (specialisation in Artificial Intelligence), 30 credits

Compulsory courses for Mathematical Statistics
If you are aiming for a degree in Mathematical Statistics, you shall, in addition to the courses above, also study the following courses.

• Stochastic Processes and Simulation, 7.5 credits (first cycle)
• Data Preprocessing and Visualisation, 7.5 credits
• Design of Experiments and Advanced Statistical Modelling, 15 credits
• Multivariate Data Analysis, 7.5 credits
• Big Data and high-dimensional data analysis, 7.5 credits
• Thesis Project for the Degree of Master of Science in Mathematical Statistics, 30 credits

Elective profile courses
Within the programme, you can choose to specialise in one of five different profiles. The profiles cover the AI areas Reasoning (Reasoning and decision-making), Learning (Machine learning, Data science) and Interaction (Human-AI interaction), and an application area (Intelligent Robotics). Each profile provides competence for examination in either Mathematical Statistics (Data Science profile) or Computer Science with a specialisation in Artificial Intelligence (other profiles). To start the degree project in Computer Science, you are required to have taken at least two courses within a profile in addition to the compulsory courses above.

"Reasoning and decision-making"
This profile covers theories and formal methods for knowledge representation, reasoning, decision-making, inference and planning. These methods are, for example, used for autonomous, intelligent systems and decision support systems in industry and healthcare. This profile provides competence for examination in Computer Science with a specialisation in Artificial Intelligence.

• Interactivity in smart environments, 7.5 credits
• Trends in interactive intelligent environments, 7.5 credits
• Reasoning and decision-making, 7.5 credits (from Autumn 2022)
• Individual project in Artificial Intelligence, 7.5 credits
• Individual project in Artificial Intelligence, 15 credits

"Machine learning"
This profile provides knowledge of and practical experience in machine learning methods such as Bayesian methods, support vector machines, reinforcement learning, logistic regression, deep learning and its applications. The knowledge is needed to be able to apply relevant and efficient machine learning solutions for real problems for example for textual analysis, classification, prediction, image analysis etc, knowledge that is useful in both industry and third-cycle courses and study programmes. This profile provides competence for examination in Computer Science with a specialisation in Artificial Intelligence.

• Interactivity in smart environments, 7.5 credits
• Trends in interactive intelligent environments, 7.5 credits
• Natural Language Processing, 7.5 credits
• Individual project in Artificial Intelligence, 7.5 credits
• Individual project in Artificial Intelligence, 15 credits

"Human-AI interaction"
This profile covers Human-AI interaction, multi-agent systems, socially intelligent systems, theories of man as an agent in activity, as well as ethical, legal and social aspects to consider in developing AI. Within the industry, this knowledge is essential to develop the digital agents of the future, intelligent environments and new ways for people to cooperate with AI-based systems. This profile provides competence for examination in Computer Science with a specialisation in Artificial Intelligence.

• Interactivity in smart environments, 7.5 credits
• Human Robot Interaction, 7.5 credits
• Trends in interactive intelligent environments, 7.5 credits
• Cognitive Interaction Design, 7.5 credits
• Individual project in Artificial Intelligence, 7.5 credits
• Individual project in Artificial Intelligence, 15 credits

"Intelligent Robotics"
This profile covers Intelligent Robotics and especially Human-Robot Interaction, Machine Cision and language technologies. Within the industry, this knowledge is beneficial in developing service robots in for example healthcare. This profile provides competence for examination in Computer Science with a specialisation in Artificial Intelligence.

• Interactivity in smart environments, 7.5 credits
• Human-Robot Interaction, 7.5 credits
• Project course in Machine Vision, 7.5 credits
• Natural Language Processing, 7.5 credits
• Individual project in Artificial Intelligence, 7.5 credits
• Individual project in Artificial Intelligence, 15 credits

"Data Science"
This profile covers methods for data processing and statistical analysis of large amounts of data. Within the industry, there are many applications of knowledge in data science where data sets are processed, for example in the manufacturing industry or the energy sector. This profile provides competence for examination in Mathematical Statistics.

Elective courses
Elective courses are a selection of courses that Umeå University offers within the scope of the programme and where the student chooses which of these courses to enrol in. The student is guaranteed a seat in one of these courses, provided that the entry requirements for the courses in question are met. However, the student is not guaranteed a seat in courses of their first choice. Entry requirements are listed in each respective course syllabus. The range of elective courses offered can vary from one year to another.
 
Computer Science

• Efficient algorithms, 7.5 credits
• Introduction to Database Management, 7.5 credits (first cycle)
• Database System Principles, 7.5 credits
• Student Conference in Computer Science, 7.5 credits

Mathematics and Mathematical Statistics

• Time Series Analysis and Spatial Statistics, 7.5 credits
• Discrete Modelling, 7.5 credits
• Enterprise Risk Management, 15 credits
• Statistics Methods in Genetics, 7.5 credits
• Monte Carlo Methods for Financial Applications, 7.5 credits
• Statistical learning for sparse data (from Autumn 2022)

Other subjects

• Activity Theory, 7.5 credits
• 'Design-Build-Test', project course for engineers, 15 credits
• Convolutional Neural Networks with Applications in Medical Image Analysis, 7.5 credits

Free electives
Free electives within the programme are open for applications from all. Free electives can be studied at Umeå University or at other higher education institutions in Sweden or abroad.

Degree Project/independent project
The degree project concludes the programme and may be initiated once the entry requirements in the course syllabus are met. The programme leads to several possible qualifications depending on which track you choose during the course of the programme (see overview in Tables 1 and 2 in the study plan). In the degree project comprising 30 credits (or 15 credits for a Degree of Master (60 credits)), the student shall apply the knowledge acquired during their studies and orally and in a written report/thesis present the result of the work. The work shall include some form of subject-specific specialisation within the field. The degree project is usually completed individually. However, it is also occasionally permitted for two students to cooperate on a degree project.
The degree project can advantageously be completed in cooperation with the business world. A client supervisor shall be appointed and act as the student's day-to-day contact and support during the course. A thesis supervisor at the university shall always be appointed and be responsible for ensuring that the required subject specialisation is achieved. The report shall be linguistically and stylistically designed to ensure its quality is equivalent to reports published within the university and the industry. The report shall include an English abstract and an English translation of the title. Alternatively, the entire report may be written in English.

Deferment of studies

Information on deferment of studies is available on Umeå University's website.

Approved leave from studies

Information on approved leave from studies is available on Umeå University's website.

Discontinuation

Information on discontinuation is available on Umeå University's website.

Other

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.

Outline

Valid from: HT21

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

Table 2. Overview of possible specialisations for a two-year programme with Degree of Master (120 credits) in Computer Science with a specialisation in Artificial Intelligence, or a Degree of Master (120 credits) in Mathematical Statistics. Courses in italics are examples of elective courses, other courses are compulsory. 
 

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