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Course syllabus - Introduction to Applied AI for Manufacturing Industry

Scope

3 credits

Course code

PPU483

Valid from

Autumn semester 2023

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements).

Main area(s)

Product and Process Development

School

School of Innovation, Design and Engineering

Ratified

2023-01-19

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2023-11-17
    • Latest update: 2023-11-17

    Other Materials

    The course materials will consist of online literature provided throughout the duration of the class.

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Objectives

The aim of this course is to provide the students with basic knowledge and practice in handling and processing data to build AI applications for the manufacturing industry. 

Learning outcomes

Upon completion of the course shall the student be able to: 
1. Understand the basic concepts of big data and machine learning 
2. Understand the most important prerequisites and challenges in using big data and machine learning within the manufacturing industry 
3. Understand and use suitable tools for the analysis of big data and explain the result
4. Demonstrate the ability to practically and theoretically translate his / her knowledge within applied AI in manufacturing industry applications

Course content

The course contains lectures, project work, assignments and laboratory sessions to enable the student to get knowledge of: 
- Basics of big data and machine learning
. Machine learning algorithms and tools
- Application of big data and machine learning to the manufacturing industry

Specific requirements

75 credits in mechanical engineering, production engineering, product and process development, computer engineering and/or computer science or equivalent or 40 credits in engineering/technology and at least 2 years' experience in full-time employment in a relevant area within industry.
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), 1 credit, marks Fail (U) or Pass (G) (examines learning outcomes 1-2)
Project (PRO1), 1 credit, marks Fail (U), 3, 4 or 5 (examines learning outcome 4)
Laboratory work (LAB1), 1 credit, marks Fail (U) or Pass (G) (examines learning outcome 3)

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.

Study guide

Grade

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course overlaps 3 credits with PPU433 Cloud Based Data Management and Analytics, 3 credits with PPU442 Big Data and Machine Learning on Cloud Platform for Industrial Applications, 3 credits with PPU485 Big Data and Machine Learning on Cloud Platform for Industrial Applications and 3 credits with PPU440 Big Data and Cloud Computing for Industrial Applications.