TRIS22 - Research Methods for Intelligent Systems

Course Code:	TRIS22
Confirmed:	Nov 28, 2024
Valid From:	Jan 18, 2027

Education Cycle:	Second-cycle level
Disciplinary domain:	Technology
Subject group:	Computer Technology
Specialised in:	A1F Second cycle, has second-cycle course/s as entry requirements
Main field of study:	Computer Science

Intended Learning Outcomes (ILO)

On completion of the course the student shall:

Knowledge and understanding

display knowledge of the current state-of-the-art in AI, machine learning and data science
demonstrate knowledge and comprehension of 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
display specialised methodological knowledge of the main field of study

Skills and abilities

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

Judgement and approach

demonstrate the ability to compare and evaluate different representations and algorithms for intelligent agents
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 the ability to have insight into the possibilities and limitations of research, its role in society and the responsibility of the individual for how it is used
demonstrate the ability to identify the personal need for further knowledge and take responsibility for his or her ongoing learning

Content

The course comprises of lectures and seminars. The lectures broadly cover the theoretical foundations of typical research approaches in artificial intelligence and related areas as well as common research methods and ways of reporting research findings. The seminars cover academic communication, state-of-the-art development in artificial intelligence from a scientific perspective, and open research questions and challenges.

The course includes the following elements:

Information retrieval and literature surveys
Qualitative and quantitative approaches to research
Computer science as a scientific paradigm
Research methods in computer science
Basic and applied research in artificial intelligence
Ethical considerations
Assessment of scientific quality

Type of instruction

Lectures are conducted in the traditional format, which means that they are aimed at transmission of course content whereby the focus is on the delivery of the material by the lecturer. Each lecture is 90 minutes with 15 minutes break half-way. The seminars are planned and moderated by the students based on specifications from the course responsible and materials described in the course memo.

Language of instruction is English.

Entry requirements

Passed courses at least 90 credits within the major subject Computer Engineering, Computer Science, Electrical Engineering (with relevant courses in Computer Engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering, and completed course Machine Learning, 7,5 credits or equivalent. Proof of English proficiency is required.

Examination and grades

The course is graded 5, 4, 3 or U.

Registration of examination:

Name of the Test	Value	Grading
Seminars	1.5 credits	G/U
Assignments	1.5 credits	G/U
Academic Report ¹	4.5 credits	5/4/3/U

¹Determines the final grade of the course, which is issued only when all course units have been passed.

COURSE SYLLABUS