AI in Industry

The course provides a comprehensive overview of artificial intelligence (AI) applications in modern manufacturing. It covers AI methods and tools, including machine learning, deep learning and artificial neural networks, as well as software such as MATLAB, Python, and TensorFlow. Topics in machine learning include supervised and unsupervised learning methods, such as decision trees, support vector classification, regression, and clustering. The course also teaches the application of global optimization and multi-criteria decision-making algorithms.Through practical case studies, students will learn how to apply AI for predictive maintenance, product and production process optimization, risk assessment, and analysis. The course also addresses the evaluation of ethical considerations.

NB! This course will take place in autumn semester 2026/2027 which starts on 31st of August and ends on 24th of January (you can find that information under start date section). TalTech's timetables for autumn semester 2026 will be published at the end of June via tunniplaan.taltech.ee.

After completing this course, the student:

• knows the fundamental methods of artificial intelligence and machine learning, and their application in modern manufacturing;
• analyzes and solves problems in manufacturing using supervised and unsupervised machine learning methods;
• uses machine learning tools (e.g., MATLAB, Python, TensorFlow) to solve practical problems such as predictive maintenance or defect detection;
• is able to apply global optimization methods to enhance the efficiency of manufacturing processes;
• applies multi-criteria decision-making methods to develop practical solutions (e.g., risk assessment, prioritization of key indicators, process selection, etc.);
• knows the ethical aspects of AI implementation.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

Motivation to learn recent and upcoming methods and techniques.

• George Chryssolouris , Kosmas Alexopoulos , Zoi Arkouli, 2023, A Perspective on Artificial Intelligence in Manufacturing.
• Edited by John Soldatos, 2024, Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI (Open access).
• Edited by Jaydip Sen, Sidra Methab, 2021, Machine Learning - Algorithms, Models and Applications (Artificial Intelligence)
• Edited by Masoud Soroush, Richard D Braatz, 2024, Artificial Intelligence in Manufacturing: Applications and Case Studies
• Edited by Kaushik Kumar, Divya Zindani, J. Paulo Davim, 2024, Artificial Intelligence in Mechanical and Industrial Engineering).
• Edited by by Ganesh M. Kakandikar, Dinesh G. Thakur, 2020, Nature-Inspired Optimization in Advanced Manufacturing Processes and Systems.
• Edited by Kim Phuc Tran, 2023, Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges.
• Alaa Khamis, 2024, Optimization Algorithms: AI Techniques for Design, Planning, and Control Problems.
• Anand J. Kulkarni and Suresh Chandra Satapathy, 2020, Optimization in Machine Learning and Applications.
• Vadim Smolyakov, 2024, Machine Learning Algorithms in Depth.
• Kejriwal, 2022, Artificial Intelligence for Industries of the Future.

lectures, practices