Official course description, subject to change:
Preliminary info last published 17/05-21

Machine Learning

Course info
Language:
English
ECTS points:
15
Course code:
BSMALEA1KU
Participants max:
97
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course semester
Semester
Efterår 2022
Start
29 August 2022
End
30 December 2022
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
This course gives a fundamental introduction to machine learning (ML) with an emphasis on statistical aspects. In the course, we focus on both the theoretical foundation for ML and the application of ML methods.
Description

The course enables students to analyse machine learning algorithms, implement abstractly specified machine learning methods in an imperative programming language, modify machine learning methods to analyse practical datasets, and convey the results.

The course gives an overview of fundamental concepts and reasoning behind machine learning methods, based especially on a probabilistic and decision-theoretic framework. Further, we discuss a broad range of classical machine learning methods such as 

  • Linear models for regression and classification
  • Neural networks
  • Kernel methods
  • Mixture Models and EM
  • Ensemble methods
  • Decision trees
Formal prerequisites

The course is mandatory for third semester in the BSc in Data Science and assumes students to have followed the courses Applied Statistics and Linear Algebra and Optimisation, or something equivalent.

Some prior experience with Python will also be assumed.

Intended learning outcomes

After the course, the student should be able to:

  • Discuss, clearly explain, and reflect upon central machine learning concepts and algorithms.
  • Choose among and make use of the most important machine learning approaches in order to apply (match) them to practical problems.
  • Implement abstractly specified machine learning methods in an imperative programming language.
  • Combine and modify machine learning methods to analyse practical dataset and convey the results.
Ordinary exam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.