Machine Learning with Big Data

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 every day 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.

About the course

Module 1 -  Introduction and background

Introduction is intended to review Machine learning (ML) and Big Data processing techniques and its related subtopics with the 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, clustering analysis, deep learning and other 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.

Learning Outcomes

  • The student should after course completion be able to:
  • describe the basic principles of machine learning and big data
  • demonstrate the ability to identify key challenges to use big data with machine learning
  • show the ability to select suitable machine Learning algorithms to solve a given problem for big data.
  • demonstrate the ability to use tools for big data analytics and present the analysis result

Related industrial challenges addressed in the course

  • Structure and evaluate the vast amount of data to make sure that it is feasible to solve the customer problem.
  • Acquire new, previously unknown, knowledge from routinely available huge amount of industrial data to support effective automation, decision-making etc. in industries.
  • Transform knowledge acquired from the data into machines. This knowledge can be used by automated systems in various fields and provide economic values.

Course Syllabus

For Course Syllabus use course code DVA453 in the  search field

Teacher

Shahina Begum
+46 21 10 73 70
Mobyen Uddin Ahmed
+46 21 10 73 69

Prompt

The course is included in the Prompt project, which is partly funded by the Knowledge Foundation. You can find more information about Prompt here .