Machine Learning

2022/2023

Prerequisite/Recommended prerequisite for participation in the module

The module builds on mathematical knowledge obtained in the bachelor courses “Linear Algebra” and “Introduction to Probability and Applied Statistics“ (bachelor in IT, Communication and New Media), or similar.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

Must have knowledge about:

  • data modelling in form of preparing data, modelling data, and evaluating and disseminating the results.
  • key machine learning concepts such as feature extraction, cross-validation, generalization and over-fitting, prediction and curse of dimensionality.
  • different machine learning principles, algorithms, techniques and be able to define and describe fundamental problems and consequences within machine learning.
  • basic recommender system principles, techniques, algorithms and be able to define and describe fundamental problems and consequences within these.

Skills

Must be able to:

  • discuss how the data modelling methods work and describe their assumptions and limitations.
  • map practical problems to standard data models such as regression, classification, density estimation, clustering and association mining.
  • select and apply a range of different machine learning algorithms and techniques on specific problems.
  • select and apply the basic recommender system algorithms and techniques on specific problems 
    OR 
    select and apply relevant machine learning algorithms and techniques for detection of cyber attacks or anomalous behaviour in cyber systems

Competences

Must have the competency to:

  • solve machine learning related problems in a practical context.
  • apply machine learning algorithms and analyse the results

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

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

Facts about the module

Danish titleMaskinlæring
Module codeESNICTEK2K7A
Module typeCourse
Duration1 semester
SemesterSpring
ECTS5
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Copenhagen
Responsible for the module

Organisation

Study BoardStudy Board of Electronics and IT
DepartmentDepartment of Electronic Systems
FacultyTechnical Faculty of IT and Design