NFYK20002U Applied Machine Learning

Volume 2024/2025

MSc Programme in Physics



The course will give the student an introduction to and a basic knowledge of Machine Learning (ML) and its use in various parts of data analysis. The focus will be on application through examples and use of computers, and be project based.

The course will cover the following subjects:

  • Introduction to Machine Learning
  • Types of problems suitable for ML and their typical solutions.
  • Types of problems not suitable for ML
  • Classification and Regression
  • Supervised vs. Unsupervised training
  • Dimensionality Reduction
  • ML performance and loss functions
Learning Outcome


The student should in the course obtain the following skills:

  • Understand the use of ML in data analysis
  • Be able to apply ML algorithms to (suitable) dataset
  • Be able to optimise the performance of the ML algorithm
  • Be capable of quantifying and comparing ML performances


The student will obtain knowledge about ML concepts and procedures, more specifically:

  • The fundamental methods used in ML.
  • Various Loss-Functions and Goodness measures.
  • The most commonly used three and neural net based ML algorithms.
  • Examples of ML usage on various types of data.


This course will provide the students with an understanding of ML methods and knowledge of (structured) data analysis with ML, which enables them to analyse data using ML in science and beyond. The students should be capable of handling data sparcity, non-uniformities, along with unbalanced and categorical data.

See Absalon for final course material.

Basic knowledge of programming is required corresponding to a bachelor course in programming for physicists.
The student should be familiar with the general line of thinking in programming, and be able to build own programs independently. Elementary mathematics (calculus, linear algebra, and combinatorics) is also required.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures, exercises by computers (mostly), discussion, and small projects.
The course is identical to NFYK18000U Big Data Analysis.
It is not allowed to pass both courses.

It is expected that the student brings a laptop.
  • Category
  • Hours
  • Lectures
  • 56
  • Preparation
  • 22
  • Theory exercises
  • 28
  • Project work
  • 100
  • Total
  • 206
7,5 ECTS
Type of assessment
Written assignment, final project
Continuous assessment
Type of assessment details
The final grade is given based on the continuous assessment consisting of three assignments (40%) as well as on the final group project (60%).
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
several internal examiners

The re-exam form will be oral based on a new or re-submitted final project (30 minutes, no preparation) counting for 60% of the final grade. If the assignments (continuous evaluation, 40%) were already approved during the course, they can be re-used, or new assignments can be submitted. Project and assignments must be submitted no later than 3 weeks before the re-exam.

Criteria for exam assesment

see learning outcome