Official course description:
Full 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
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Assistant Professor, Head of study programme
Teacher
Postdoc
Teacher
Assistant Professor
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Course semester
Semester
Efterår 2021
Start
30 August 2021
End
31 December 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 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.
Learning activities

The course will comprise around 10 weeks of lectures and exercise sessions and around 4 weeks of project work.

Course literature

Aurélien Géron (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly.

Christopher Bishop (2006). Pattern Recognition and Machine Learning. Springer. 

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:
A project report on the analysis of a dataset using machine learning methods.
Group submission:
Group
  • 2-3
Exam duration per student for the oral exam:
20 minutes
Group exam form:
Individual exam : Individual student presentation followed by an individual dialogue. The student is examined while the rest of the group is outside the room.

Time and date
Ordinary Exam - submission Mon, 3 Jan 2022, 08:00 - 14:00
Ordinary Exam Mon, 24 Jan 2022, 09:00 - 21:00
Ordinary Exam Tue, 25 Jan 2022, 09:00 - 21:00
Ordinary Exam Wed, 26 Jan 2022, 09:00 - 21:00
Ordinary Exam Thu, 27 Jan 2022, 09:00 - 21:00
Ordinary Exam Fri, 28 Jan 2022, 09:00 - 21:00