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

Objectives

This course aims to provide students with a comprehensive understanding of fundamental AI concepts that enable the efficient use of big data to support smart decision-making in various engineering fields. Students will learn how to develop AI solutions that address industry-specific challenges in manufacturing, sustainable energy systems, building engineering, and product and process development. The course will equip students with the knowledge and skills to apply machine learning techniques and big data processing workflows, with a focus on data analytics to optimize operations and enhance performance in these sectors. By the end of the course, students will be prepared to tackle real-world engineering problems using advanced AI-driven approaches to improve efficiency, sustainability, and innovation.

Learning outcomes

After completion of the course the student should be able to:

1. Explain the fundamental principles of big data and analyze key data-related challenges in engineering domains such as manufacturing, energy systems, and the built environment.
2. Describe core concepts of machine learning and identify the prerequisites for applying big data and machine learning in engineering applications.
3. Apply appropriate methods for processing and analyzing complex engineering datasets and interpreting the results to support decision-making.
4. Evaluate design considerations for developing scalable and production-grade machine learning solutions in industrial settings.
5. Demonstrate the ability to develop AI-based solutions using cloud platforms within the contexts of product and process development, building engineering, or energy engineering.

Course content

The course includes lectures, assignments, laboratory sessions, and project work designed to provide students with an understanding of how big data and machine learning can be applied through cloud platforms in various engineering domains. Emphasis is placed on real-world data challenges and applications relevant to manufacturing, product and process development, building engineering, and sustainable energy systems. Students will explore data-driven approaches for supporting engineering decision-making, operational efficiency, and sustainable development.

Specific requirements

120 credits in engineering including 5 credits programming in Python. In addition, Swedish course 3 or Swedish level 3 and English course 6 or English level 2 are required. For courses given entirely in English exemption is made from the requirement in Swedish course 3 or Swedish level 3.

Examination

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

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course overlaps 7,5 credits with PPU485 Big Data and Machine Learning on Cloud Platform for Industrial Applications, 7,5 credits with PPU442 Big Data and Machine Learning on Cloud Platform for Industrial Applications, 7,5 credits with PPU433 Cloud Based Data Management and Analytics and 3 credits with PPU483 Introduction to Applied AI for Manufacturing Industry.