Machine Learning Practical (MLP): Semester 1, 2015

[course descriptor]



The coursework-based Machine Learning Practical (MLP) is focused on the implementation and evaluation of machine learning systems. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.

In 2015-16 the course will focus on deep neural networks.

MLP requires mathematical ability (calculus, linear algebra, and probability) and programming ability (the course will be based on Python using Numpy). Some previous experience of machine learning is is extremely helpful.
Undergraduates: If you have taken Informatics 2B and IAML, and can program, you are qualified to do the course.
It is also recommended to take MLPR.

MLP 2015: Deep Neural Networks

This year the MLP course will focus on deep neural networks for the classification of handwritten digits using the well-known MNIST dataset. Using a Python software framework that we shall provide, and a series of iPython notebooks, the aim of the course is to train multi-layer neural neural network classifiers and convolutional network classifiers to address this handwritten digit classification problem. There will be a series of eight weekly lectures to provide the required theoretical support to the practical work.


The first lecture will be in Week 1: Wednesday 23 September at 10am, in F.21, 7 George Square.


  1. Wednesday 23 September. Introduction to MLP. Linear networks. Gradient descent. Slides / 6-up slides
  2. Wednesday 30 September. Stochastic gradient descent. Classification. Slides / 6-up slides
  3. Wednesday 7 October. Multilayer networks. Slides / 6-up slides
  4. Wednesday 14 October. Introduction to coursework 1; Generalisation (part 1). Slides / 6-up slides
  5. Wednesday 21 October. Learning rate schedules, genersalisation (part 2), more on softmax. Slides / 6-up slides
  6. Wednesday 28 October. Hidden unit transfer functions, autoencoders, and pretraining. Slides / 6-up slides
  7. Wednesday 4 November. Convolutional networks (1). Slides / 6-up slides
  8. Wednesday 11 November. Convolutional networks (2). Slides
  9. Friday 4 December. Recurrent neural networks. Slides. (Extra lecture, optional. This lecture will take place at 10:00, Friday 4 December 2016, Teviot Lecture Theatre, Medical School, Doorway 5.

You can discuss and ask questions about these lectures on the online MLP Forum.


Currently four lab sessions are scheduled, students are expected to attend one of these.

The lab material will be made available using github at You do not require a github login to use this. Labs will use iPython notebook

There are many Python/numpy tutorials on the web, I think that this is a good one:


Please make sure you have read and understood



Additional material

Review articles (including material not covered in this course)

This page maintained by Steve Renals.
Last updated: 2016/08/10 17:04:34UTC

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