Study Guide

Objective

The course will cover different AI-based approaches for industrial automation and autonomy. The student will get knowledge about deep learning techniques, such as object detection, image classification and segmentation and their use, for example, in quality control and supervision. The student will learn the working principles of forecasting techniques and generative methods. The student will also learn how to collect data and to train, validate and test model performance. The student will understand the importance of data quality and have insights into different pre-processing methods.

Knowledge and understanding
After completing the course, the student should be able to:
- understand how to use deep learning techniques to solve industrial problems.
- suggest a potential solution approach for a specific problem.

Skills and abilities
After completing the course, the student should be able to:
- implement a deep learning solution for practical problems.
- apply and experiment with different deep learning techniques.

Evaluation ability and approach
After completing the course, the student should be able to:
- discuss the opportunities and potential to apply deep learning techniques to solve industrial problems.
- evaluate and report on the results of the performance of a deep learning model.

Content

The course will cover deep learning techniques such as object detection, predictive maintenance, forecasting models, and generative design.

Materials

The study material will be provided by the lecturer.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

Grade 1: Satisfactory skills in industrial artificial intelligence

Assessment criteria, good (3)

Grade 3: Good skills in industrial artificial intelligence

Assessment criteria, excellent (5)

Grade 5: Excellent skills in industrial artificial intelligence

Qualifications

No prerequisites.