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
The course includes four modules:
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.
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.
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.
Data analytics with tools: Presents open source tools e.g., KNIME and Spark with examples that guide through the basic analysis of big data.
You will learn
- 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.
Below you find the entry requirements for the course. If you do not fulfill the requirements, you can get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etcetera. Read more in Application information below.
Occasions for this course
Autumn semester 2023
2023-08-28 - 2024-01-14 (part time 25%)
Independent of location
Number of mandatory occasions including examination: 0
Number of other physical occasions: 0
Course syllabus & literatureSee course plan and literature list (DVA453)
90 credits of which at least 60 credits in Computer Science or equivalent, including at least 15 credits in programming. 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.
Questions about the course?
If you have any questions about the course, please contact the Course Coordinator.
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?
Many courses requires that you have previous academies studies, but we can validate work experience to determine whether you have the qualifications 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 Word, 45.5 kB, opens in new window.
• Download a template for Employers certificate Word, 38 kB, opens in new window.
If you have any questions regarding eligibility or application please send an e-mail to email@example.com