Course syllabus - Machine Learning With Big Data 7.5 credits

Maskininlärning med Big Data

Course code: DVA453
Valid from: Autumn semester17 Autumn semester18
Level of education: Second cycle
Subject: Informatics/Computer and Systems Scie...
Main Field(s) of Study: Computer Science,
In-Depth Level: A1N (Second cycle, has only first-cycle course/s as entry requirements),
School: IDT
Ratification date: 2017-01-31


The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced everyday at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.

Learning outcomes

After completing the course, the student shall be able to:

1. describe and understand the basic principles of machine learning and big data
2. demonstrate the ability to identify key challenges to use big data with machine learning
3. show the ability to select suitable machine Learning algorithms to solve a given problem for big data
4. demonstrate the ability to use tools for big data analytics and present the analysis result 

Course content

Module 1. Introduction and background: introduction is intended to review Machine Learning (ML) and BigData processing techniques and related subtopics with focus on the underlying themes.
Module 2. Case studies: presents case studies from different application domains and discuss key technical issues e.g., noise handling, feature extraction, selection, and learning algorithms in developing such systems.
Module 3. Machine learning techniques in big data analytics: this module consists of basic understanding of learning theory, association rule learning, clustering analysis and classification techniques appropriate for development work and issues in construction of systems using Big data.
Module 4.  Data analytics with tools: presents open source tools e.g., KNIME and Spark with examples that guide through the basic analysis of big data.

Teaching methods

Online video-based lectures, problem-based learning, assigned readings of scientific articles (reading, searching the web), chat rooms/discussion forum.

Specific entry requirements

120 credits of which at least 80 credits in Computer Science and / or equivalent, including at least 30 credits in computer science or software development. In addition, at least 18 months of documented work experience in software development or related areas. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.


Written assignment (INL1), (Module 1), 1,0 credit, (examines the learning objective 1), Marks Fail (U) or Pass (G)
Written assignment (INL2), (Module 2), 1,5 credits, (examines the learning objective 2), Marks Fail (U) or Pass (G)
Written assignment (INL3), (Module 3), 2,0 credits, (examines the learning objectives 3), Marks Fail (U) or Pass (G)
Exercise (ÖVN1), (Module 4), 3 credits, (examines the learning agreement 4), Marks Fail (U) or Pass (G)


Rules and regulations for examinations


Two-grade scale

Course literature is not yet public.