Artificial Intelligence for Industrial Analytics
Syllabus, Master's level, 1TS321
- Code
- 1TS321
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Computer Science A1N, Industrial Engineering and Management A1N, Technology A1N
- Grading system
- Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
- Finalised by
- The Faculty Board of Science and Technology, 9 November 2022
- Responsible department
- Department of Civil and Industrial Engineering
Entry requirements
165 credits including 10 credits in computer programming, 20 credits in mathematics, and 5 credits in statistics and probability theory. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Learning outcomes
On completion of the course the student shall be able to
- apply fundamental tools within modern artificial intelligence (AI) with a focus on intelligent agents and systems for industrial analytics,
- give an account of the role and potential of modern AI methods for applications in Industrial Analytics,
- explain, select between, and use different artificial intelligence (AI) building methods, for applications within industrial analytics.
Content
Introduction to artificial intelligence (AI) and intelligent agents/systems in industrial analytics, including cyber-physical systems (i.e. mixtures of hardware and software). Optimization based on automated differentiation and nature-inspired techniques. Fitting of predictive/intelligent models to collected data. Passive learning agents in the form of artificial neural networks and Gaussian processes, for classification and regression problems. Active learning agents and systems that themselves choose new experiments/observations, with applications in global optimization and predictive modeling. Reinforcement learning where intelligent agents learn optimal decision strategies (for example regarding automatic control and maintenance of industrial processes) based on repeated interactions with the environment. Industrial application examples.
Instruction
Lectures, seminars, laboratory sessions and supervision of project.
Assessment
Written examination, and written and oral presentation of project work.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding targeted pedagogical support from the disability coordinator of the university.