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 this 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.
- 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.
- 90 credits of which at least 60 credits in Computer Science or equivalent, including at least 15 credits in programming.
- In addition, English course A/English course 6 is required.
You can also apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc. Read more in Application information below.
You’ll find the entry requirements in the course description. After submitting your application, the next step is to submit documentation to demonstrate your eligibility for the course. Most academic credentials from Sweden are retrieved automatically. Wait a few days after submitting your application - if you still can’t see your academic credentials om My pages, please upload them.
If you have studied in another country, you must provide transcripts of your academic studies and of your English proficiency. Exactly what you need to submit and how, depends on several factors. You can read more on universityadmissions.se or antagning.se.
If the course requires work experience, you need to provide an employer’s certificate. You can download a template for employer’s certificate below.
No academic qualifications?
The course require that you have previous academies studies, but we validate work experience to assess if you have the knowledge that is equal to the eligibility requirements for the course.
If you don’t have the formal qualifications required, please send in a certificate of employment (current or previous) and a CV/Description of competence that describes your educational and professional background. Please include a short description of your work experience, not only the work title.
Use the CV/ Description of competence template below and fill in the information requested.
You can also use our template for Employers certificate if you like.
• Download a template for CV/Description of competence
• Download a template for Employers certificate
If you have any questions regarding eligibility or application please send an e-mail to email@example.com
The courses are part of the Prompt project where MDH offers courses at master's level. The courses are given online without physical meetings and are flexible in time and space so that they can be combined with professional life.For companies that want to collaborate on competence development