Official course description for:
Full info last published 31/07-19

Machine Learning

Course info
Language:
English
ECTS points:
15
Course code:
BSMALEA1KU
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
Bachelor
Programme:
Bachelor of Science in Data Science
Staff
Course manager
Part-time Lecturer
Teacher
Associate Professor
Teacher
Postdoc
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Course semester
Semester
Efterår 2019
Start
26 August 2019
End
31 January 2020
Abbreviation
20192
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 as well as 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 subjects covered include linear models for regression and classification, neural networks, kernel methods and graphical models.

  • Linear models for regression and classification
  • Neural networks
  • Kernel methods
  • Graphical models
  • Mixture Models and EM
  • Continuous Latent Variable
  • Models for Sequential Data
  • Ensemble methods

Formal prerequisites
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 covey the results.
Learning activities

Description: The first 10 weeks will consist of lectures and exercise sessions. The last 4 weeks will consist of project work.

Mandatory activities
  • Active participation in exercise sessions (details will be given though LearnIT)
  • Five peer graded hand-ins. (deadlines are disrubted over the semester, details will be given though LearnIT)
If the mandatory active participation is not approved, an alternative test will be held at the end of the course (see details on LearnIT). 

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

Ethem Alpaydin: Introduction to Machine Learning, third edition. The MIT Press. ISBN 978-0-262-02818-9

Ordinary exam
Exam type:
D: Submission of written work with following oral, external (7-trinsskala)
Exam variation:
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted. The group has a shared responsibility for the content of the report.
Exam description:
The group makes their presentations together and afterwards the students participate in the dialogue individually while the rest of the group is outside the room. (Mixed exam 2). The exam will last 20 min per students. The groups must consist of 2-3 persons.