Machine Intelligence

2020/2021

Prerequisite/Recommended prerequisite for participation in the module

Knowledge of discrete mathematics, algorithms and data structures

Content, progress and pedagogy of the module

Learning objectives

Knowledge

  • basic techniques and methods in machine intelligence including their theoretical foundations and practical applications
  • the use of correct technical notation and terminology

Skills

  • use basic techniques presented in the course to solve a specific problem
  • use correct technical notation and terminology in both writing and speech
  • be able to explain the key principles and algorithms presented in this course

Competences

  • able to evaluate and compare different machine intelligence techniques and methods based on a specific problem

Type of instruction

The teaching is organized according to the general teaching methods for the education, cf. chapter 3

 

 

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 examMachine Intelligence
Type of exam
Written or oral exam
ECTS5
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe 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

Facts about the module

Danish titleMaskinintelligens
Module codeDSNCSITK103
Module typeCourse
Duration1 semester
SemesterAutumn
ECTS5
Language of instructionDanish and English
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyTechnical Faculty of IT and Design