Deep Learning

3 hp

Date: 3-5 May 2023 in Lund

Registration deadline: March 31

Lecturers: Amandus Krantz and Christian Balkenius

Course Description

The course Deep Learning for non-programmers introduces Deep Learning on a conceptual and intuitive level, without getting tangled in technical implementation details such as mathematics or programming. The course assumes no mathematical or programming knowledge of the students.

The aim of the course is to provide non-technical students with (1) the knowledge to participate in discussions on Deep Learning and (2) a high-level overview of the field, without necessarily having to learn how to build and develop Deep Learning models.

Topics that will be covered in the course:

– Anatomy of the Artificial Neural Network (ANN)

– Relationship between Deep Learning and Neuroscience

– Training of deep neural networks

– Supervised and unsupervised learning

– Convolutional Neural Networks (CNN)

– Reinforcement learning

– Algorithmic bias

– Applications of deep learning

After the course, the student will:

– Be able to explain and discuss basic deep learning concepts

– Have a conceptual understanding of basic deep learning


3 May

13.15-14.00 Introduction (AK/CB)

14.15-15.00 What is an artificial neural network? 1 (AK)


15.15-16.00 What is an artificial neural network? 2 (AK)

16.15-17.00 Fooling a network (CB)


4 May

10.15-11.00 Convolutional networks 1 (AK)

11.15-12.00 Convolutional networks 2 (AK)


13.15-14.00 Reinforcement learning 1 (CB)

14.15-15.00 Reinforcement learning 2 (CB)


15.15-16.00 Network architectures 1 (AK)

16.15-17.00 Network architectures 2 (AK)

5 May

9.15-10.00 Bias (CB)


10.15-11.00 Deep learning applications(AK)

11.15-12.00 Conclusion (AK/CB)

19-23 May

Peer reviewing

26 May

Deadline for examination paper

5 pages


Christian Balkenius (CB)

Amandus Krantz (AK)


Please register no later than March 31! After this date we cannot guarantee a room at Hotel Lundia.