DS807: Applied machine learning

Study Board of Science

Teaching language: English
EKA: N340075102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340075101
ECTS value: 10

Date of Approval: 19-04-2024

Duration: 1 semester

Version: Approved - active


Entry requirements

The course cannot be chosen if you have passed, registered, or have followed DSK807, or if DSK807 is a constituent part of your Curriculum.

Academic preconditions

The students are expected to be familiar with the following topics:

  • Regression analysis.
  • Basic unsupervised methods, including principal component analysis and clustering.
  • Sampling techniques such as the Bootstrap, cross-validation, and data split (train and test), and how (and when) they should be applied.
  • Basic understanding of “modern” approaches to statistical learning, including decision trees.
  • Application of the above techniques in a programming language.

This knowledge can be obtained through the courses DS804: Data mining and machine learning; DS805: Multivariate Statistical Analysis and, if possible, DS810: Data Driven Decision Making.

Course introduction

The aim of the course is to enable the student to apply the most commonly used methods in machine learning.

The focus of the course to:

  • Give the competence to setup a complete applied machine learning analysis from beginning to end.
  • Give skills to perform classification and predictions using statistical and deep learning methods and to make critical assessments of the results.
  • Give knowledge and understanding of a broad range of machine leaning techniques and to access their strengths and weaknesses when applied to data of varying types.

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:

  • Develop an understanding of the fundamental concepts of machine learning, including algorithms, models and practices.
  • Implement good methods and practices for effective deployment of machine leaning systems
  • Acquire practical competencies and hands-on experience in applying machine learning methods in quantitative and qualitative workflows using a variety of data types.


The following main topics are included in the course. Under each topic presented the main focus will be on the applications of the models and methods:

•    Classical statistical learning tools and their applications (shallow learners):

  1. Support vector machines
  2. Decision trees, boosting, random forests, and gradient boosting
  3. Ensembling

•    Deep Learning Methods and their applications:

  1. Fully connected neural networks 
  2. Convolutional neural networks
  3. Recurrent neural networks
  4. Training, regularization and optimization
  5. Autoencoders and variational autoencoders
  6. GANs
  7. Transformers and Generative AI

Models, applications and data will be inspired by cases posted on Kaggle and Hugging face.


  • Deep Learning with Python by Francois Collet (ISBN10: 9781617294433)

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)


Handed out in December and submitted at the end of January


Written take-home exam




Second examiner: Internal


7-point grading scale


Full name and SDU username


Normally, the same as teaching language


One Week

Examination aids

To be announced during the course

ECTS value


Indicative number of lessons

45 hours per semester

Teaching Method

Indicative number of teaching hours covers more than 45 hours per semester, divided into llectures and exercises. 

Activities during the study phase:

  • Attending lectures, including computer lab session
  • Solving ad hoc assigments
  • Self study of various parts of the course material.

Teacher responsible

Name E-mail Department
Christian Møller Dahl cmd@sam.sdu.dk Econometrics and Data Science


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.