SWERIM

This 40-hour course is a navigational guide to the field of neuromorphic information processing and sensing. Brains are several orders of magnitude more energy efficient than digital computers and networks. How is that possible? World-wide efforts to mimic the neurosynaptic architecture of the brain is forming a new generation of event-based sensor and machine learning systems, which are expected to disrupt the current AI technology developments. This course introduces the unconventional information processing principles implemented in neuromorphic technologies as well as state-of-the-art software and tools.

Target audience: Professionals in technology-driven sectors such as edge computing, automotive, robotics, monitoring, and AI including engineers, managers and academics who are considering integrating neuromorphic technologies in their systems, products or research.

Prerequisite-knowledge: Basic understanding of machine learning, ordinary differential equations, electric circuits, and programming in python or a similar imperative language.

Course content:

The course aims to bridge the gap between current engineering practice and the demands of industries/academics considering using neuromorphic technologies and spiking neural networks. This course aims to provide an opportunity for upskilling, so that professionals can remain competitive in their roles and can expand their future career prospects. The modular structure of the course allows for individuals to learn at their own pace, making it accessible for those with varying time constraints and professional commitments. Spanning five complementary modules spread across five weeks, this course is designed to empower you with the skills and knowledge necessary to endeavour into the exciting field of neuromorphic information processing and sensing.

How the course will be conducted:

The course combines self-study and online meetings. The course is structured as follows: Tutorials (30 hours), computer exercises with quizzes (6 hrs), online meetings (4 hours).

Dates and times for the online meetings:

2024-mm-dd kl. 13:00-14:00

2024-mm-dd kl. 13:00-14:00

To pass the course you need to:

Complete the mandatory quizzes, which partially involve basic computer simulation exercises in the form of Jupyter notebooks, as well as participate in the four online meetings. This way you will assess the acquired knowledge and practice identifying the need for and seeking further knowledge.

Presentation of the teachers:

Fredrik Sandin, Professor in Machine Learning with over ten years of experience in brain-like machine learning and neuromorphic computing.

Foteini Liwicki, Associate Professor in Machine Learning with long experience of brain-machine interfaces and language processing.