Practical Machine Learning Course (2019): This page is maintained by Dr Adrian Bevan

This web page contains a set of slides and example scripts used for a Practical Machine Learning (PML) summer school course delivered at QMUL in 2019. These resources are prepared using several Python packages including NumPy, MatplotLib and Tensorflow. As the TensorFlow versions change from time to time, and this is beyond my control you may find different versions of the slides and example scripts to match given versions of that code.

Syllabus: This course will cover the following technical aspects: introductory python including use of NumPy arrays and plotting functionality, the use of TensorFlow for computation, including multilayer perceptrons and deep networks (including convolutional neural networks). The following concepts will be discussed: linear discriminants, perceptrons, activation functions (binary threshold, logistic, hyperbolic tangent, relu), neural networks, multilayer perceptrons, convolutional neural networks, training and validation for supervised learning problems, dropout, maxpooling, optimisation; function approximation, classification and regression.

Requirements:This has been written for

Ancillary notes

Slides - to be updated

Example Code - to be updated

Example Data

Guest Lectures

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Maintained by Adrian Bevan. Last update on July-2019