Specialisation Data Science
Compulsory courses
Introduction to data science Year 1 / Semester 1
The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.
Read more about Introduction to data science
Study points: 10
Course coordinator:
Antorweep Chakravorty
Statistical modeling and simulation Year 1 / Semester 1
This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.
Read more about Statistical modeling and simulation
Study points: 10
Course coordinator:
Stein Andreas Bethuelsen
Machine learning Year 1 / Semester 2
The course focuses on methods for learning the underlying structures from data and to train models that can make predictions when presented with new data. Such predictions can typically involve the discrimination between different categories of data, or pattern classification, which will be the main focus of this course.
Read more about Machine learning
Study points: 10
Course coordinator:
Trygve Christian Eftestøl
Probability and Statistics 2 Year 2 / Semester 3
Basic issues in probability. Presentation of a number of commonly used probability distributions. Short introduction to extreme-value statistic. Estimation, in particular the maximum likelihood principle,and confidence intervals in various situations. Brief introduction to Bayesian statistics.Stochastic processes, in particular Poisson processes and Markov processes. Theory and areas for applications of the various methods will be covered.
Read more about Probability and Statistics 2
Study points: 10
Course coordinator:
Jörn Schulz
Data Mining and Deep Learning Year 2 / Semester 4
The purpose of this course is for students to gain knowledge and practical experience of data mining and deep learning techniques. The course will prepare the students with a deep knowledge of technologies and be able to prepare large-scale data for data mining (pre-processing) and use a number of data mining and deep learning methods to extract actionable knowledge. The course will provide the opportunity for students to learn state-of-the-art data mining and deep learning algorithms and tools. The students will get hands-on experience to try these tools on real data.
Read more about Data Mining and Deep Learning
Study points: 10
Course coordinator:
Vinay Jayarama Setty
Data-intensive Systems Year 3 / Semester 6
The course will provide a strong basis in administrative, programing, and algorithm design aspects of data intensive systems.
Read more about Data-intensive Systems
Study points: 10
Course coordinator:
Tomasz Wiktorski
Master's thesis in Computer Science Year 4 / Semester 7
The master thesis is an independent project in which you will apply the knowledge acquired during your studies on solving a given assignment. It is through this assignment that you will show your abilities and qualities as a coming engineer.
The assignment will normally be carried out during the last semester of your studies. At this stage you will have acquired the knowledge and know-how needed for accomplishing a relevant assignment in your studies.
Read more about Master's thesis in Computer Science
Study points: 30
Course teacher:
Morten Mossige
Select one course 5th semester
Security and Vulnerability in Networks Year 3 / Semester 5
Basic problems and challenges, cryptography, cryptographic protocols, secure software and malicious code, access control, network security, security assessment and management, regulations and laws. Guest lectures give relevance to best practice.
Read more about Security and Vulnerability in Networks
Study points: 10
Course coordinator:
Rong Chunming
Information retrieval and text mining Year 3 / Semester 5
The course offers an introduction to techniques and methods for processing, mining, and searching in massive text collections. The course considers a broad variety of applications and provides an opportunity for hands-on experimentation with state-of-the-art algorithms using existing software tools and data collections.
Read more about Information retrieval and text mining
Study points: 10
Course coordinator:
Krisztian Balog
Select 2 courses 7th semester
Discrete Simulation and Performance Analysis Year 4 / Semester 7
This course first introduces Petri net theory; then, the theory is used for modeling, simulation and performance analysis of discrete event systems.
Read more about Discrete Simulation and Performance Analysis
Study points: 10
Course coordinator:
Reggie Davidrajuh
Project in Computer Science Year 4 / Semester 7
The project gives practice in solving a research assignment within the computer science area.
Read more about Project in Computer Science
Study points: 10
Course coordinator:
Leander Nikolaus Jehl
Statistical learning Year 4 / Semester 7
Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis.
Read more about Statistical learning
Study points: 10
Course coordinator:
Jan Terje Kvaløy
Specialisation Reliable and Secure Systems
Compulsory courses
Security and Vulnerability in Networks Year 1 / Semester 1
Basic problems and challenges, cryptography, cryptographic protocols, secure software and malicious code, access control, network security, security assessment and management, regulations and laws. Guest lectures give relevance to best practice.
Read more about Security and Vulnerability in Networks
Study points: 10
Course coordinator:
Rong Chunming
Statistical modeling and simulation Year 1 / Semester 1
This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.
Read more about Statistical modeling and simulation
Study points: 10
Course coordinator:
Stein Andreas Bethuelsen
Distributed Systems Year 1 / Semester 2
The course gives insight into both theoretical and practical aspects of distributed computing systems, with particular emphasis on techniques for building fault tolerant systems.
Read more about Distributed Systems
Study points: 10
Course coordinator:
Veronica del Carmen Estrada Galinanes
Wireless Communications Year 2 / Semester 3
Wireless communication has achieved ever-greater popularity in the last two decades, and has already become an important part of our everyday lives. This subject is aimed towards giving an overview of the most important principles and state-of-the-art technologies that are making wireless communications possible.
Read more about Wireless Communications
Study points: 10
Course teacher:
Gianfranco Nencioni
Algorithm Theory Year 2 / Semester 4
This course is about algorithm theory and complexity theory, which includes the following topics: Graphs and graph algorithms, greedy algorithms, dynamic programming, linear programming, and NP-completeness.
Read more about Algorithm Theory
Study points: 10
Course coordinator:
Reggie Davidrajuh
Discrete Simulation and Performance Analysis Year 3 / Semester 5
This course first introduces Petri net theory; then, the theory is used for modeling, simulation and performance analysis of discrete event systems.
Read more about Discrete Simulation and Performance Analysis
Study points: 10
Course coordinator:
Reggie Davidrajuh
Master's thesis in Computer Science Year 4 / Semester 7
The master thesis is an independent project in which you will apply the knowledge acquired during your studies on solving a given assignment. It is through this assignment that you will show your abilities and qualities as a coming engineer.
The assignment will normally be carried out during the last semester of your studies. At this stage you will have acquired the knowledge and know-how needed for accomplishing a relevant assignment in your studies.
Read more about Master's thesis in Computer Science
Study points: 30
Course teacher:
Morten Mossige
Select one course 6th semester
Data-intensive Systems Year 3 / Semester 6
The course will provide a strong basis in administrative, programing, and algorithm design aspects of data intensive systems.
Read more about Data-intensive Systems
Study points: 10
Course coordinator:
Tomasz Wiktorski
Machine learning Year 3 / Semester 6
The course focuses on methods for learning the underlying structures from data and to train models that can make predictions when presented with new data. Such predictions can typically involve the discrimination between different categories of data, or pattern classification, which will be the main focus of this course.
Read more about Machine learning
Study points: 10
Course coordinator:
Trygve Christian Eftestøl
Select 2 courses 7th semester
Project in Computer Science Year 4 / Semester 7
The project gives practice in solving a research assignment within the computer science area.
Read more about Project in Computer Science
Study points: 10
Course coordinator:
Leander Nikolaus Jehl
Information retrieval and text mining Year 4 / Semester 7
The course offers an introduction to techniques and methods for processing, mining, and searching in massive text collections. The course considers a broad variety of applications and provides an opportunity for hands-on experimentation with state-of-the-art algorithms using existing software tools and data collections.
Read more about Information retrieval and text mining
Study points: 10
Course coordinator:
Krisztian Balog
Blockchain Technologies Year 4 / Semester 7
This subject gives insight into different technologies and models for blockchain systems and hands-on experience developing smart contracts.
Read more about Blockchain Technologies
Study points: 10
Course coordinator:
Leander Nikolaus Jehl
Image Processing and computer vision Year 4 / Semester 7
Image processing is used in a growing number of applications in our daily lives as well as in research. Image processing is utilized for medical images, radar images, natural images, seismic data etc. in addition to robot vision. Thus, an understanding of classical image processing is useful in many fields.
Elements from both traditional image processing and computer vision are used to construct systems for robot (machine) vision. There is a rapid development in this field and applications are found in both industry and research. There are also many products with camera and software for processing of visual data. The objectives of this course are that the student should gain a fundamental understanding of Image Processing and Computer Vision with applications in Robot Vision (Machine Vision).
Read more about Image Processing and computer vision
Study points: 10
Course teacher:
Kjersti Engan

This is the study programme for 2020/2021. It is subject to change.