Why Learn Machine Learning and Optimisation?

In this post I hope to convince the reader that machine learning and optimisation are worthwhile fields for a software developer in industry to engage with.

I explain the purpose of this blog and argue that we are in the midst of a machine-learning revolution.


When I first started coding as a teenager in the early 1990s, the future looked certain to be shaped by artificial intelligence. We were told that we’d soon have “fifth generation” languages that would allow for the creation of complex software applications without the need for human programmers. Expert systems would replace human experts in every walk of life and we’d talk to our machines in much the same way Gene Roddenberry imagined we should.


Unfortunately, this model of reality didn’t quite go to plan. After many years of enormous research and development expense — mainly focused in Japan — we entered another AI winter. The future was left in the hands of a handful of diehard academics, while the software industry mostly ignored AI research.

The good news is that the AI winter is now well and truly over.  The technology has been slowly but surely increasing its influence on mainstream software development and data analytics for at least a decade and 2015 has been billed as a breakthrough year by media sources such as Bloomberg and and Wired magazine.

Whether we realise it or not, most of us use AI every day. In fact, AI is responsible for all of the coolest software innovations you’ve heard of in recent years. It is the basis for autonomous helicopters, autonomous cars, big data analytics, google search, automatic language translation, targeted advertising, optical character recognition, speech recognition, facial recognition, anomaly detection, news-article clustering, vehicle routing and product recommendation, just to list the few examples I could name at the time of writing.

As a field, artificial intelligence has been deeply rooted in academia for decades, but it is quickly becoming prevalent in industry.  We are at the dawn of the AI revolution and there has never been a better time to start sciencing up your skill set.

This blog, is here to help and, as its name suggests, will focus on two important and complementary sub-fields of AI: Machine Learning and Optimisation. The intention is to explain both topics in a language that software developers in industry can easily understand, with or without a background in hard computer science.

I believe this is an important addition to the discourse on these topics because most of the sources you’re likely to come across assume a strong existing knowledge of linear algebra, calculus, statistics, probability, information theory and computational complexity theory: the language of academic computer science.  This is unsurprising, given that the techniques were mostly developed by computer scientists, mathematicians and statisticians, but it can unfortunately be a barrier to a lot of people getting started.

The intention here is to remove that barrier by describing the various techniques using familiar, medium-level programming languages.  The posts that follow will not shy away from the theory, but no assumptions will be made with respect to prior understanding of mathematics or computer science and code snipets will accompany any mathematical descriptions.

Author: James Burkill

Software engineer and student of all things AI. LinkedIn: https://ie.linkedin.com/in/james-burkill-459a1513

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