Course content

This course explores the integration of Artificial Intelligence (AI) within the manufacturing industry, emphasizing data-driven decision-making and process optimization. The course would provide the foundations to understanding of AI concepts and applications. Through a combination of theory, applied techniques, and case studies, students will learn to utilize machine learning to enhance manufacturing efficiency, improve quality control, and manage complex data sets. This course covers key AI concepts, data management, statistical foundations, and machine learning applications specific to manufacturing.

The course begins with an Introduction to AI in Manufacturing, covering core AI concepts, historical context, and specific applications in manufacturing, such as quality monitoring and operational control. Students will explore the benefits and challenges of implementing AI, gaining insights into its potential to enhance efficiency and decision-making in the industry.

Next, the Data Management for Machine Learning module teaches students essential data cleaning, processing, and preparation techniques to ensure high-quality datasets for AI. Emphasizing data integrity, this module highlights how data quality directly affects the accuracy of machine learning outcomes.

In the Basic Statistics Refreshment module, students gain foundational knowledge in descriptive statistics, probability theory, and regression analysis, along with dimensionality reduction techniques. These skills are critical for analyzing manufacturing data and supporting data-driven decisions.

The course concludes with the Machine Learning Module, where students learn about classification, prediction, and other machine learning types. By examining applications like predictive maintenance and quality control, students see how machine learning can drive improvements in manufacturing operations.

Learning outcome

Upon completing this course, students will acquire:

1. Knowledge:

• Define and explain core AI concepts and its role in the manufacturing industry.
• Understand data quality principles, preprocessing techniques, and their significance in machine learning outcomes.
• Comprehend foundational statistical methods and machine learning algorithms applicable to data-driven manufacturing.

1. Skills:

• Apply data cleaning, normalization, and transformation techniques to prepare datasets for machine learning.
• Utilize descriptive statistics, probability theory, and regression analysis to interpret data relevant to manufacturing contexts.
• Implement machine learning algorithms for predictive maintenance and quality control applications within manufacturing.

1. General Competence:

• Develop critical thinking skills to analyze the potential and limitations of AI in manufacturing.
• Demonstrate an understanding of data-driven approaches for process optimization and in production.
• Collaborate and communicate effectively on AI-related topics, data management, and machine learning applications, bridging the gap between technical and operational perspectives in manufacturing.

Learning methods and activities

Teaching Methods:

• Interactive seminars utilizing the flipped classroom approach
• E-learning modules for flexible learning
• Project-based work to apply theoretical knowledge
• Collaborative group work to enhance teamwork skills
• Hands-on practical exercises using existing databases

The course is designed to be accessible to both on-campus and remote students. Each student can choose the pedagogical arrangement that best fits their needs. Seminars will be conducted on campus and are also available via streaming through Blackboard Collaborate/MS Teams, with recordings accessible through NTNU's learning management system. Tutoring will be available both on-campus and online at scheduled times. The medium of instruction is Norwegian/English, and all assignments, reports, and documentation must be submitted in Norwegian/English.

Specific conditions

Recommended previous knowledge

Basic concepts of probability and statistics. Basic concepts of mathematics

Course materials

Relevant articles and reports will be given at course start