Big Data Analysis

Course content

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

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
  • ML performance
  • Big Data management and data access

MSc Programme in Physics

MSc Programme in Nanoscience

MSc Programme in Environmental Science

MSc Programme in Physics w. minor subject

Learning outcome


The student should in the course obtain the following skills:

  • Understand the use of ML in data analysis
  • Use ML on a given (suitable) dataset
  • Be able to attempt 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 Cost-Functions and Goodness measures.
  • The most commonly used ML algorithms.


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, and categorical data.

Lectures, exercises by computers (mostly), discussion, and small projects.

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 needed.

It is expected that the student brings a laptop.

7,5 ECTS
Type of assessment
Continuous assessment, Final project, 3 weeks
The final grade is given based on the continuous evaluation (50%) as well as on the final project (50%).
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
several internal examiners
Criteria for exam assessment

see learning outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 56
  • Theory exercises
  • 28
  • Preparation
  • 122
  • English
  • 206