DS843: Introduction to Artificial Intelligence

The Study Board for Science

Teaching language: Danish or English depending on the teacher
EKA: N340162102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340162101
ECTS value: 10

Date of Approval: 04-11-2024


Duration: 1 semester

Version: Archive

Internal Course Code

DS843

Comment

Course is co-read with AI501: Introduction to Artificial Intelligence

Entry requirements

The course cannot be followed if the student has passed AI501 or DM879, or if the student has AI501 or DM879 mandatory in their curriculum.

Academic preconditions

The student is expected to have basic understanding of programming, algorithms and mathematical proofs, obtainable e.g. by having followed DS820 and DS830.

Course introduction

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 prepares the students for further advanced courses in specific topics in Artificial Intelligence.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • provide knowledge of theories and experimental methods within the central areas of computer science
  • provide the ability to apply the learned methods to concrete problems
  • provide the ability to illuminate hypotheses made on a qualified theoretical background and be critical of one's own and others' research results and scientific models
  • provide the ability to develop new variants of the learned methods where the specific problem requires it

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.

Content

  • Overview of Artificial Intelligence
  • Search techniques
  • Knowledge, reasoning and planning

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

June

Tests

Portfolio exam

EKA

N340162102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Duration

Skriftelig eksamen - 4 hours

Examination aids

The exam is with limited aids. Only the following aids are allowed:

  • language translation dictionaries (e.g. Danish/English, Danish/German etc) in "ordbogsprogrammet" (the dictionary programme) from http://www.ordbogen.com/ in electronic form. The browser version is not allowed. See the complete list of which dictionaries are allowed in the separate "Instruction to ordbogen dot com". All dictionaries other than the allowed dictionaries must be switched off in “ordbogsprogrammet” (the dictionary programme).

    Internet is not allowed. However, you may access the course page in itslearning to open system "DE–Digital Exam" and complete any tests within the system.

    ECTS value

    10

    Additional information

    Portfolio exam consisting of:
    (a) a group orject with written report
    (b) written exam during the exam period

    The final grade is a combination of the performance in the sritten exam and the project, where the performance in the project can increase or decrease the overall grade by one level. 

    Indicative number of lessons

    56 hours per semester

    Teaching Method

    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.

    Teacher responsible

    Name E-mail Department
    Jacopo Mauro mauro@imada.sdu.dk Institut for Matematik og Datalogi

    Timetable

    Administrative Unit

    Institut for Matematik og Datalogi (datalogi)

    Team at Registration

    NAT

    Offered in

    Odense

    Recommended course of study

    Profile Education Semester Offer period

    Transition rules

    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.