Online workshop: An introduction to Machine Learning

You’ve read so much about Machine Learning and finally want to see for yourself what all the hype is about? Then our introduction to Machine Learning online workshop is your chance to get your feet wet!

This workshop is designed for people who want to gain an understanding of Machine Learning (ML), specifically:

  • What is ML?
  • What are the different types (supervised, unsupervised, reinforcement)?
  • What are the main algorithms used (Clustering, Neural Nets etc.)
  • What are the strengths and weaknesses of ML?
  • What processes are involved in performing ML?
  • How does the relatively simple ML that we can perform in this workshop compare with what, for example, Tesla is doing?

This is a workshop, so there will be a practical element. This is mostly performed in Python which seems to have become the language of choice for ML.

However, we are aware that some attendees may not be familiar with Python and, for example, may not have it, or an IDE (Integrated Development Environment) installed on their machines. So, we will supply working code and those with a knowledge of Python and a suitable installation can get it working and then try altering the code to experiment with the various parameters.

For those who do not have Python installed, we will simply do this as a demonstration. We will load up the code, show you it running and working. Then we can alter some of the parameters to show you how these influence the learning that is performed.

Unfortunately we won’t be able to walk attendees through the process of downloading Python, an IDE and the required libraries in the given time, because we know from experience that doing so soaks up so much time that it would change the focus of the workshop away from ML and onto the tool we happen to have chosen to demonstrate it with.

If you need help to set things up before the day of the workshop though, you can get in touch and we’ll do our best to assist you.

Location: Date: December 9, 2021 Time: 9:00 am - 5:00 pm Mark Whitehorn Prof. Mark Whitehorn