Prerequisite/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

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
• basic techniques and methods in machine intelligence including their theoretical foundations and practical applications

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

• be able to evaluate, compare and select techniques and methods within machine intelligence based on a specific problem

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

It is expected that the student uses 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
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