Machine learning is the science of getting computers to act without being programmed.
Machine learning can be used in many different ways, such as: Improving search engine rankings, Improving the quality of online services, Identifying spam messages.
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Sports analytics is a fascinating topic and I have written about it many times, and also released courses in that area, in topics such as how to predict sports outcomes. A particularly interesting piece of work I did in the past was to study the use of machine learning for Read more…
Supervised PCA is a very useful, but under-utilised, model.There are many cases in machine learning where we deal with a large number of features. There are many ways to deal with this problem. If we suspect that many of these features are useless, then we can apply feature selection techniques such as: Univariate Read more…
Deep learning against humans Creating machines that can perform tasks better than humans has always been the dream behind artificial intelligence. A London-based company called DeepMind, had a breakthrough in 2015, when it produced a deep neural network, called AlphaGo, that can beat a professional Go player. This was the first Read more…
Sports analytics remains one of my favourite topics, as I have both done research in this area, but also released courses for the public. One of my favourite pieces of research, was when I researched into whether Twitter can be used to predict football outcomes. The article can be found Read more…
Human-level concept learning I am a huge fan of Joshua Tenenbaum and his research. Recently, he published in collaboration with Brenden Lake and Ruslan Salakhutdinov a new piece of research regarding “Bayesian Program Learning“. The paper is actually entitled “Human-level concept learning through probabilistic program induction”. Bayesian program learning is an answer to one-shot learning. Read more…
Deep neural networks can be complicated to understand, train and use. Deep learning is still, to a large extent, an experimental science. This is why getting some input on the best practices can be vital in making the most out of the capabilities that neural networks offer. This article presents Read more…
The differences between machine learning and statistics Machine learning and statistics are the two core disciplines for data analysis. Both fields provide the scientific background for data science and data scientists will usually have trained in one of the two. However, much has been said about the differences between the Read more…
Issues with performance measures in machine learning When testing a predictive model, choosing the correct performance measure is imperative for making sure our model works correctly. In machine learning literature, however, it is common to use measures because they have always been used in the past, without really judging whether they Read more…
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