Collaborating Authors

Student and Faculty Guide – 10 easy steps to get up and running with Azure Machine Learning


My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.

Explainable AI and the Future of Machine Learning


As the'AI era' of increasingly complex, smart, autonomous, big-data-based tech comes upon us, the algorithms that fuel it are getting under more and more scrutiny. Whether you're a data scientist or not, it becomes obvious that the inner workings of machine learning, deep learning, and black-box neural networks are not exactly transparent. In the wake of high-profile news reports concerning user data breaches, leaks, violations, and biased algorithms, that is rapidly becoming one of the biggest -- if not the biggest -- sources of problems on the way to mass AI integration in both the public and private sectors. Here's where the push for better AI interpretability and explainability takes root. Already a focal point of machine learning consulting and a notable topic in the 2019 AI discussions, it's only likely to accelerate and become one of the central conversations of 2020 regarding the questions of both security and ethics of artificial intelligence.

DEAP documentation -- DEAP 1.1.0 documentation


DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and SCOOP. The following documentation presents the key concepts and many features to build your own evolutions. – Machine Learning Experiment Management


Machine learning has made a significant shift from academia to industry in the last decade. The combination of large datasets, computing resources and significant investments have allowed researchers to push state-of-the-art results on most machine learning benchmarks. However, we are missing the fundamental tools to manage machine learning teams and processes. Over the past year, we conducted short interviews with over 200 data scientists from a variety of companies and research institutes. We asked them about their processes and their team dynamics.

[Working Life] The hard road to reproducibility


Early in my Ph.D. studies, my supervisor assigned me the task of running computer code written by a previous student who was graduated and gone. I had to sort through many different versions of the code, saved in folders with a mysterious numbering scheme. There was no documentation and scarcely an explanatory comment in the code itself. It took me at least a year to run the code reliably, and more to get results that reproduced those in my predecessor's thesis. Now that I run my own lab, I make sure that my students don't have to go through that.