Machine Learning

2020/2021

Content, progress and pedagogy of the module

Learning objectives

Knowledge

Key models in machine learning and their associated learning and inference techniques, such as:

  • Statistical linear models
  • Markov chains and hidden Markov models
  • Support Vector machines
  • Neural Net
  • Probabilistic Graphic Models
  • Matrix factorization

The use of machine learning methods in selected fields of application, such as:

  • Web and network mining
  • Recommendation Systems
  • Computer games
  • Image analysis
  • Text mining

Skills

  • be able to apply advanced techniques from machine learning to the construction of intelligent systems

Competences

  • to understand advanced machine learning methods for designing intelligent systems
  • to analyze their usefulness and impact in solving specific tasks

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 examMachine Learning
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 titleMaskinlæring
Module codeDSNDATFK213
Module typeCourse
Duration1 semester
SemesterSpring
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