Recommended prerequisite for participation in the module

It is recommended that the student has knowledge of discrete mathematics, algorithms and data structures

Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

Learning objectives

Knowledge

The student should gain knowledge of the following theories and methods:

• problem solving using search and inference
• model-based decision making
• inference under uncertainty
• learning from experience and learning from data

Skills

• use correct technical notation and terminology in writing as well as speech
   
• apply basic techniques presented in the course to solve a specific problem
   
• explain key principles and algorithms presented in the course

Competences

• based on a concrete problem, must be able to select, use and evaluate techniques and methods within machine intelligence

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

Name of exam	Machine Intelligence
Type of exam	Written or oral exam
ECTS	5
Permitted aids	Aids (if any) will be posted on the course page in MOODLE
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Contact: The Study board for Computer Science at cs-sn@cs.aau.dk or 9940 8854