GRA 4160 Predictive Modelling with Machine Learning

GRA 4160 Predictive Modelling with Machine Learning

Course code: 
GRA 4160
Data Science and Analytics
Course coordinator: 
Vegard Høghaug Larsen
Course name in Norwegian: 
Predictive Modelling with Machine Learning
Product category: 
MSc in Data Science for Business
2025 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

The vast amount of widely available data allows machines to solve challenging tasks without explicitly being programmed to do so, often outperforming existing methods based on domain knowledge and/or human experts.

In this course we focus on basic machine learning methods, both for supervised and unsupervised learning. We will look at how exactly machines "learn" from the data, and how they use the knowledge learned during training to solve tasks of interest.

The course covers both the theory (looking at the methods more rigorously than introductory machine learning courses) and the practice of machine learning (using Python).

Learning outcomes - Knowledge

By the end of the course, the student should be able to:

  • Explain the covered machine learning algorithms,
  • Describe their applications, advantages, disadvantages, and limitations,
  • Choose the appropriate learning methods to solve a given problem of interest.
Learning outcomes - Skills

By the end of the course, the student should be able to:

  • Implement the basic machine learning methods from scratch,
  • Design and build learning systems using existing state-of-the-art machine learning libraries,
  • Train, evaluate, tune, and test these systems,
  • Present and defend the results of their work in a professional and academic manner.
General Competence

During the course, students will also improve their general programming and presentation skills, and practice working in teams.

Course content
  • Recapitulation (supervised vs. unsupervised learning, linear regression and classification).
  • Advanced regression and classification.
  • Clustering.
  • Neural networks (feedforward neural networks, backpropagation algorithm, stochastic gradient descent).
  • Practical aspects of neural networks (e.g., architectures, weights initialization, normalization, regularization, dropout, optimized training methods).
  • Ensemble methods (boosting and bagging).
  • Model selection and assessment.
Teaching and learning activities
  • Organized classes will be a mixture of lectures and working on in-class assignments.
  • Students are expected to prepare for the organized classes by watching selected videos online and/or reading selected texts.
  • Students are also expected to work on assignments outside of the class, either individually or in small groups.
  • Mini project (in groups of 2-4 students).

Software tools: Python (including its state-of-the-art machine learning packages).

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to get a grade in the course.

Continuous assessment will no longer exist as an examination form from autumn 2023. For questions regarding previous results, contact InfoHub.


All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

Programming (preferably Python), intermediate math (including linear algebra, basic calculus, and basics of theory of probability), or similar types of courses.

Exam category: 
Form of assessment: 
Submission other than PDF
Exam/hand-in semester: 
First Semester
30 Hour(s)
Exam code: 
GRA 41602
Grading scale: 
Examination when next scheduled course
Exam category: 
Activity, Oral
Form of assessment: 
Exam/hand-in semester: 
First Semester
Group (2 - 4)
20 Minute(s)
Presentation and defense of the mini project
Exam code: 
GRA 41603
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
Student workload
28 Hour(s)
Group work / Assignments
100 Hour(s)
Prepare for teaching
12 Hour(s)
30 Hour(s)
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.