DM577: Introduction to Artificial Intelligence
Study Board of Science
Teaching language: English
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
STADS ID (UVA): N330062101
ECTS value: 7.5
Date of Approval: 12-10-2022
Duration: 1 semester
The student is expected to have basic understanding of mathematical proofs and programming, obtainable e.g. by having followed DM549 Discrete methods for computer science or MM537 Introduction to Mathematical Methods and DM536/DM574 Introduction to Programming or DM562: Scientific Programming.
The aim of the course is to equip participants with knowledge about the basic concepts and techniques underlying intelligent computer systems. The focus is on four aspects - problem solving, reasoning, decision making and learning - and on the logical and probabilistic foundations of these activities. The course builds upon competences from DM549 and DM574, and introduces topics that are expanded upon in DM581.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- provide knowledge of a large selection of key algorithms and data structures developed in the field of computer science
- provide knowledge of knowledge of scientific theory
- provide the ability to describe, analyse and solve computer science problems by applying methods and modelling formalisms from the core areas of the subject and its mathematical support disciplines
- provide the ability to analyse advantages and disadvantages of different algorithms, especially in terms of resource consumption
- provide the ability to make and justify academically related decisions
- provide the ability to describe, formulate and disseminate issues and results to both peers and non-specialists or partners and users
- provide competence to understand and reflect on theories, methods and practices pertaining to the subject area of computer science
Expected learning outcome
At the end of this course, the student is expected to have the following competences:
- outline the basic logic principles of problem solving, reasoning, and decision making;
- describe in detail the fundamental algorithms of searching, reasoning, and decision making covered in the curriculum of the course;
- assess the applicability of basic problem solving and reasoning techniques to different problems that resemble those seen in the lectures;
- develop intelligent systems to solve concrete computational problems.
Exam element a)
Second examiner: Internal
7-point grading scale
Full name and SDU username
Normally, the same as teaching language
Not allowed, a closer description of the exam rules will be posted in itslearning.
Indicative number of lessons
The teaching method is based on three phase model.
- Intro phase: 28 hours
- Skills training phase: 28 hours, including 28 hours tutorials
Activities during the study phase: Solving small assignments, individually or in small groups.
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Recommended course of study
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.