Overview - Learning and Knowledge

Intended learning outcomes

After successfully completing this module the participant will have

• Basic knowledge in
  • Machine learning, including using current tools for both traditional machine learning and deep learning.
  • Knowledge representation and reasoning

A further goal is that the participant strengthens the network both at the host university, between universities and between academia and industry. To achieve this goal, we require that you attend the sessions that are organized. Participation over Skype or similar can be arranged.

A word about time and expectations

You come into the course with different levels of backgrounds. We have tried to cover material from basic to intermediate. Some of you will find all the content too basic and some of you will find all of it hard. Machine learning and deep learning in particular has made its way into most fields of research today. It should be in your interest to know something about this. However, to go from nothing to expert will not be possible within the scope of this course unless you are willing to invest your own time into this. There is simply not enough time. So please consider where you start from, where you want to get to and how much time you can set aside for that when you plan your work. If you have no prior experience and no more time than what the six credits correspond to you need to be realistic in the expectations on yourself. You will not be able to understand everything to the finest level of detail. In this case we suggest that you, in this encounter, focus on exploring what the tools can do for you so that you are able to make use of them in your field.

Module setup

This module covers the areas learning and knowledge. Given the importance of machine learning in every field today, that part of the module will be larger than that on knowledge representations and reasoning. There are four different activities associated with the module

• Self-study material
  
  • You will be presented with material covering sub-topics in the module.
  • The material here should be seen as a starting point for studying these topics rather than an exhaustive coverage.
  • The material acts as a reference that you can go back to later.
  • You are expected to study the material and we encourage you to do so in groups
• Local sessions (see schedule below)
  • Here you meet the other students in WASP at your university and help form the network that WASP has as an important objective. 
  • The important steps done at these sessions will be documented so that it can be done on your own as well, but we highly recommend to attend the sessions 
  • Some sessions will be to examine work. These are mandatory to attend. If you cannot attend you MUST let the local point of contact know in advance.
• 2-day session for everyone together
• Examination assignments, with deadlines that need to be respected.

Schedule