Course syllabus - Big Data and Machine Learning on Cloud Platform for Industrial Applications

Objectives

The aim of this course is to give the student insights in fundamental concepts for AI solutions that can provide efficient use of big data to support, for examples, smart decision making and predictive maintenance, within the Manufacturing industry. Furthermore, the student will learn to use tools provided by the state-of-the-art cloud platforms to develop AI solutions for industrial applications. This course also provides understanding of machine learning techniques and big data processing workflows, and data analytics using machine learning in manufacturing.

Learning outcomes

Upon completion of the course the student shall be able to:  
1. Describe the basic principles of Big Data as well as describe and understand the most important challenges within the Manufacturing industry 
2. Describe the basic principles of machine learning and describe the most important prerequisites for utilization of Big Data and Machine learning within the Manufacturing industry  
3. Understand and use suitable methods and tools for analysis of manufacturing data and explain the result  
4. Describe the high-level design decisions with production-grade machine learning 
5. Demonstrate the ability to translate his / her knowledge within cloud services for AI applications in production, logistics and product development

Course content

The course contains lectures, project work, assignments and laboratory sessions where the student gets knowledge of various applications of cloud technology and management of Big Data in the manufacturing industry.

Specific requirements

Completed courses of at least 30 credits in level G2F within product and process development. 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.

Examination

Assignment (INL1), 3 credit, marks Fail (U) or Pass (G) (examines learning outcomes 1-4) 
Project (PRO1), 3.5 credits, marks Fail (U), 3, 4 or 5 (examines learning outcomes 3, 4 and 5) 
Laboratory work (LAB1), 1 credit, marks Fail (U) or Pass (G) (examines learning outcomes 2, 3 and 5)

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass with distinction, Pass with credit, Pass, Fail

Interim Regulations and Other Regulations

The course overlaps 7.5 credits with PPU433 Cloud based Data Management and Analytics, 7.5 credits with PPU442 Big data and machine learning om cloud platform form industrial applications and 3 credits with PPU483 Introduction to Applied AI for Manufacturing Industry.