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.
Course title in Swedish
Maskininlärning med Big Data
The course is given in the autumn semester. Application opens mid-March.
After submitting your electronic application, the next step is to submit documentation to demonstrate your eligibility for the course you have applied for. In order to document your eligibility, you must provide your high school diploma and university transcript and proof of your English language proficiency.
To meet the entry requirements for this course you need to have previous academic qualifications (university studies). You will find the specific entry requirements above.
No academic qualifications?
If you do not have the formal academic qualifications needed for a specific course, you can apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc. This is also known as a validation of prior learning.
Recognition of prior learning means the mapping out and assessment of an individual's competence and qualifications, regardless how, where or when they were acquired – in the formal education system or in some other way in Sweden or abroad, just recently or a long time ago.
If you think your knowledge and competences will qualify you for this course, you will need to upload th following with your application:
- CV with description of your educational and professional background. Your CV must describe your knowledge and competences in relation to the entry requirements.
- If you refer to work experience, you need to upload an Employers certificate.
If we need more information, we will contact you.
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