**Inside the classic machine learning algorithms**

Machine Learning is not magic. You can’t just throw the data through an algorithm and expect it to provide insights. You have to prepare the data and very often you have to tune the algorithm. Some algorithms - Neural Nets, Deep Learning, Support Vector Machines and Nearest Neighbour - are starting to dominate the field. A great deal of attention is often focused on the maths behind these, and it IS fascinating.

But you don’t have to understand the maths to be able to use these algorithms effectively. What you do need

to know is how they work because that is the information that allows you to tune them effectively. This talk will explain how they work from a non-mathematical standpoint.

**Mark Whitehorn**

Mark Whitehorn specialises in Analytics and Data Science.

Mark works with national and international companies, designing Data Science solutions. In addition to his consultancy practice he has also acted as an expert witness in cases of patent infringement and for the police in cases of computer fraud.

He is a well-recognised commentator on the computer world. He is a regular contributor to The Register, has written numerous white papers and also eleven books on databases and analytics. The first one, Inside Relational Databases has been selling well since it was published in 1997 and is now in its third edition. It has also been translated into three languages.

Mark is also an associate with QA Ltd. He has developed several of the company's courses (data science and big data course, database analysis and design, MDX, Dimensional modelling) and teaches them all.

On the academic side, Mark is the emeritus Professor of Analytics at the University of Dundee where he designed and runs a Masters course in Data Science. There he also works with the prestigious Lamond labs. applying Analytics and Data Science to proteomics.

For relaxation he collects, restores and races historic cars which keeps him out of too much trouble. He only wears a tie under duress, doesn't possess a suit that fits and unashamedly belongs to the beard-and-sandals school of computing.

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