Official course description:
Full info last published 21/08-20

Machine Learning

Course info
Language:
English
ECTS points:
15
Course code:
BSMALEA1KU
Participants max:
69
Offered to guest students:
yes
Offered to exchange students:
-
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Postdoc
Teacher
Assistant Professor, Head of study programme
Teacher
Part-time Lecturer
Teacher
PhD student
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Assistant Lecturer
Course semester
Semester
Efterår 2020
Start
24 August 2020
End
31 January 2021
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
  • Decision theory
  • Mixture Models and EM
  • Ensemble methods
  • Decision trees
Formal prerequisites
  • The prerequisite required for admission to the course is Linear Algebra and Probability or similar.
  • Some prior experience with Python will be assumed.
  • It is recommended to have a basic knowledge of statistics. 
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.
Learning activities

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

Course literature

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. ISBN 978-0387-31073-2

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 20%
  • Lectures: 15%
  • Exercises: 20%
  • Project work, supervision included: 25%
  • Exam with preparation: 20%
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.
Exam submisson 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
Group submission:
Group
  • 2-3
Exam duration per student for the oral exam:
20 minutes
Group exam form:
Mixed exam 2 : Joint student presentation followed by an individual dialogue. 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.



Time and date
Ordinary Exam - submission Fri, 11 Dec 2020, 08:00 - 14:00
Ordinary Exam Mon, 11 Jan 2021, 09:00 - 19:00
Ordinary Exam Tue, 12 Jan 2021, 09:00 - 19:00
Ordinary Exam Wed, 13 Jan 2021, 09:00 - 19:00
Ordinary Exam Thu, 14 Jan 2021, 09:00 - 19:00