No small talk for big data: Software Solved spend time with Amazon scientist at Plymouth University
No small talk for big data: Software Solved spend time with Amazon scientist at Plymouth University https://www.softwaresolved.com/wp-content/uploads/2019/04/Big-Data.png 629 437 Aneeq Rehman https://secure.gravatar.com/avatar/d38e3eb3c2fd8434fcab0660899ee93a?s=96&d=mm&r=gIf you want to learn about how machine learning is being applied in the business world, e-commerce giant Amazon is a good place to start.
Software Solved’s Aneeq ur Rehman spent a week with big data experts including Amazon’s Dr Zhenwen Dai at a course at Plymouth University.
Supported by the Environmental Futures of Big Data Impact Lab, the course was led by Dr Antonia Rago alongside key academic figures from the Mathematical Sciences department including Dr Mu Nui, Dr Matthew Craven, Dr Malgorzata Wojtys and Dr Craig McNeile.
That’s a lot of Doctors – all experts in their respective fields!
Dr Zhenwen Dai, a Machine Learning Scientist at Amazon, whose, research interests include the development of scalable probabilistic models and inference methods for autonomous learning from real world data was one of the keynote speakers who shared his expertise with Aneeq and others on the course on probabilistic approaches to deal with huge volumes of data.
Aneeq, who is at Software Solved as part of our own machine learning research project, running in partnership with Plymouth University and funded by Innovate UK, said: “The course was about enhancing people’s understanding of big data and how to use and derive value from large volumes of data to make smarter decisions. Large volumes of data in machine learning often require flexible models that can imitate the way the human brain processes data and assimilates multiple sources. That was a key aspect of what we learnt.”
Topics covered included Gaussian process, machine learning and neural networks.
- Dr Mu Niu – overview of Gaussian Process, different models and how they function in theory and practise
- Dr Matthew Craven and Dr Malgorzata Wojtys – detailed explanation into various machine learning parametric methods
- Dr Craig McNeile – summary of deep learning and neural networks and running hands-on workshops and exercises to learn more about topological data analysis and high-performance computer facilities.
Aneeq, already a Masters Graduate in Data Science himself, added: “It was a good knowledge sharing platform with a blend of workshops, practical exercises and lectures. We reviewed many interesting topics such as topological data analysis, neural network design and artificial neural networks. In addition to this, we also got insights into some of the cutting-edge research and practices in the world of big data and AI.
There were also intriguing discussions over the use of neural networks and gaussian processes and good comparisons over their use and limitations. Some of it was a good refresher session for me, some of it really new and interesting.
It’s all highly relevant to the research work I’m doing at Software Solved into the application of machine learning and advanced data analytics for risk modelling and mitigation for the insurance sector.
It was great to meet with other students and learn from the academics and other organisations. It’s technical but truly fascinating and a fundamental part of how organisations will operate, interpret and derive value from the data in the 21st century- the advantages of which are manifold.”
Author: Aneeq Ur Rehman is Software Solved’s Knowledge Transfer Partnership Associate from Plymouth University
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