Artificial Intelligence/Machine Learning is becoming an increasingly important tool for mechanical and manufacturing engineers, whether you work with materials, processes or mechanical / manufacturing systems.
This course will give an introduction to emerging tools in AI/ML such as broad range of deep learning techniques that are highly relevant for the modern mechanical / manufacturing engineer. In the course valuable methods and specific industrial applications are addressed. Topics will include:
- Machine learning fundamentals
- Deep learning concepts within computer vision, natural language processing, and intelligent agents
- Deep learning methods e.g. convolutional neural (CNN) networks, recurrent neural networks (RNN), auto-encoders, LSTM, and Reinforcement Learning (RL) techniques and algorithms
- Selected industrial applications in AI/ML such as defect detection in materials and surfaces, quality inspection, predictive analytics, human-machine interaction with virtual assistants, self-learning robots and automatic optimisation of machine parameters, or similar applications
- Software, frameworks, tools and public dataset
Application implementations will be tested in the AAU Smart Lab at the Department for Materials and Production.
Prerequisites: Basic probability and statistics theory, linear algebra, machine learning, and basic programming.
Organizers: Associate Professor, Simon Bøgh, firstname.lastname@example.org
Lecturers: Associate Dimitris Chrysostomou, AAU, Assistant Professor Chen Li, AAU, Associate Professor Nestor Arana Arexolaleiba, Mondragon University, Spain (Visiting Professor AAU)
Time: 5.-9. December 2022
Place: Aalborg University, Pontoppidanstræde 101, room 1.011
Number of seats: 25
Deadline: 14 November 2022
Important information concerning PhD courses:
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.