AI and machine learning workshop

This was the annual 1 day conference for the RSE Leeds network. It was attended by 150 people; 35 people registered on the deep learning workshop and 19 on the machine learning workshop.

This is a 1 day workshop on AI and Machine Learning, in the morning there are introductory talks on the theory and practice while in the afternoon there are tutorials which need to be booked separately.

This event is organised by RSE Leeds, a network for Research Software Engineers at the University of Leeds. RSEs are people who develop software and/or support the use of computers and computer technologies for research purposes. They can work in a variety of job roles so if you think you may be one please join in: http://lists.leeds.ac.uk/mailman/listinfo/rse-network.

This meeting will start at 10:00 on Wednesday the 19th of December in the Clothworkers Central Building Speakman LT (G.89), in Clothworkers Central at the University of Leeds, UK.

09:30 Coffee and registration

10:00 Welcome Joanna Leng

10:10 Keynote David Hogg

11:00 Coffee

11:30 Learning about People from Text Alicja Piotrkowicz

12:00 The Applicability of Deep Natural Networks in Biomedical Science Alistair Droop

12:30 Lunch

Tutorials running in parallel – rooms given on the day

13:30 Tutorial on Machine Learning – An introduction to Machine Learning in Python with scikit-learn Martin Callaghan

13:30 Tutorial on Deep Learning Jonny Hancox (NVidia) and Jony Castagna (STFC)

Back in the Speakman Lecture Theatre (G.89))

16:00 Coffee

16:30 Plenary Session – What next on campus? Joanna Leng

17:00 Post workshop networking


Program in Detail

Title: Keynote

Speaker: David Hogg

Description: David Hogg will set the science for the day by introducing some of the key terms and theory along with an outline of the current research at Leeds. He is a Professor of Artificial Intelligence in the School of Computing at the University of Leeds. He is internationally recognized for his work on computer vision and works extensively across disciplinary boundaries. He has been a visiting professor at the MIT Media Lab, Pro-Vice-Chancellor for Research and Innovation at the University of Leeds, Chair of the ICT Strategic Advisory Team at the Engineering and Physical Sciences Research Council (EPSRC) in the UK, and most recently Chair of an international review panel for Robotics and Artificial Intelligence commissioned by EPSRC. He is currently Chair of the Academic Advisory Group of the Worldwide Universities Network (WUN), helping to promote collaborative research. David is a Fellow of the European Association for Artificial Intelligence (EurAI), a Distinguished Fellow of the British Machine Vision Association, and a Fellow of the International Association for Pattern Recognition.

Title: Learning about People from Text

Speaker: Alicja Piotrkowicz

Description: Natural language processing and machine learning methods can be used to infer high-level human behaviours from text artefacts. In many case you need to able to interpret the computational model and then communicate the findings to experts. This talk will present case studies of interdisciplinary projects using NLP and ML in the journalism and education domains.

Title: The applicability of Deep Neural Networks in Biomedical Science

Speaker: Alistair Droop

Description: Deep neural networks (DNNs) present very exciting possibilities in biological science. However, there are a number of worrying limitations and caveats to their general applicability. I will discuss my thoughts on both the benefits and pitfalls of DNNs for biological research.

Title: Tutorial on Machine Learning – An introduction to Machine Learning in Python with scikit-learn

Presenters: Martin Callaghan

Description: In this tutorial we will explore the basics of Machine Learning with Python and the popular scikit learn library. This is a novice level tutorial; you will be expected to have some knowledge of Python (including functions, loops, conditionals, lists and arrays) but no explicit knowledge of machine learning. Through a series of hands-on exercises and a sample data exploration using the ‘Pima Indians’ teaching dataset, we will explore:

  • The differences between supervised and unsupervised learning
  • Loading, inspecting and understanding a dataset
  • Data cleaning and transformation
  • Splitting a dataset into ‘training’ and ‘test’ datasets
  • Feature scaling and why we do it
  • How to choose a model to make a prediction
  • Choosing parameters and making a prediction

Title: Tutorial on Deep Learning

Presenters: Jonny Hancox (NVidia) and Jony Castagna (STFC)

Description: A short lecture on the deep learning frameworks from Jonny Hancox, an experienced researcher at NVidia. This is followed by a hands on training from Jony Castagna, an NVidia Ambassador. You will need to bring a laptop to do this.

Title: Plenary Session – What next on campus?

Facilitator: Joanna Leng

Description: To discuss the future eg possibility of setting up a user group and if we can arrange it the possibility of running training on campus.