Deep Learning for Automation, 4.5 hp
In this course, students will learn about deep learning architectures. They will go through some of the popular deep neural network (DNN) algorithms. They will apply these to solve a set of machine learning challenges such as classification and clustering. They will learn the use of one of the common DNN packages (such as Tensorflow or PyTorch). They will also learn about the potential of DNN in the field of automation.
FACTS
CYCLE
Second cycle
ENTRY REQUIREMENTS
General entry requirements and approved result from the following course/courses: PFA600-Programming for Automation and MFA600-Mechatronics for Automation and IAI600-Introduction to Artificial Intelligence and Machine Learning or the equivalent.
PACE OF STUDY
Part-time
TYPE OF INSTRUCTION
On Campus
PROGRAMME/COURSE DATE
TEACHING HOURS
Daytime
APPLICATION DEADLINE
15 April 2026
APPLICATION CODE
HV-E1988
START/END
From v.46 2026 to v.02 2027
Do you need help?
Entry requirements, selection, admission, study counselling and other questions