Philip Sterne has solid foundations in both the theoretical and practical application of Machine Learning. With his Ph.D. in Physics from Cambridge University, he has contributed to the understanding of statistical inference in random graphs. In his commercial career, he has built machine learning applications in banking and online retail.
Any time you have noisy data where you would like to see the underlying trend then you should think about using Gaussian processes. They will smooth out any noise and give you a great visualisation of the error bars as well. Rather than fitting a specific model to the data, Gaussian processes can model any smooth function.
I will show you how to use Python to:
- fit Gaussian Processes to data
- display the results intuitively
- handle large datasets
This talk will gloss over mathematical detail and instead focus on the options available to the python programmer. There will be code posted to github beforehand so you can follow along in the talk, or just scoop up the useful bits afterwards.