Practical Machine Learning

About

Machine learning influences modern life in many different avenues and is silently revolutionising the way we live and work. We can see the influence of machine-learning algorithms in social media, web search engines, mobile device spell checkers and self-driving cars. This course will give you an introduction to machine learning using the Python programming language and the TensorFlowTM programming toolkit from Google. No programming background is assumed, however if you want to take this course, you should be familiar with using computers.

This course is taught by scientists using machine learning for data analysis at CERN’s Large Hadron Collider and will allow you to work on practical examples from both general and physics-based problems. Examples will be drawn from a variety of problems in order to allow you to build up an understanding of the tools and how to use them. This will prepare you for a mini-project analysing data from a particle physics experiment to complement the examples encountered earlier in the course.

Course leader

Dimitris Kalogiros

Target group

Undergraduate Students

Course aim

This is a practical course that provides you with an introduction to the concepts of machine learning and the application of algorithms to several types of available data samples. In order to achieve this, you will be introduced to the Python programming language and key concepts related to the TensorFlowTM programming toolkit. You will learn how to train machine-learning algorithms and evaluate their performance on image data and scientific data from the Large Hadron Collider. We will develop your programming skills so that you can explore the potential benefits of deep-learning algorithms.

Fee info