Machine Learning Practical (MLP) 2017-18: Coursework

Coursework 1: Available 16 October 2017, Submit 16:00 30 October 2017, Feedback 18 November 2017. Worth 10%.

Coursework 1 description

This coursework is concerned with training multi-layer networks to address the MNIST digit classification problem. It builds on the material covered in the first three lab notebooks and the first four lectures. It is highly recommended that you complete the first three lab notebooks before starting the coursework. The aim of the coursework is to investigate variants of the ReLU activation function for hidden units in multi-layer networks, with respect to the validation set accuracies achieved by the trained models.

The code for the coursework is available on the course Github repository on a branch mlp2017-8/coursework_1. This also includes a LaTeX template (see the report directory) for the report that you will need to submit. Full details in the Coursework 1 document.
Coursework 2: Available Monday 6 November 2017, Submit 14:00 Wednesday 29 November 2017, Feedback 20 December 2017. Worth 25%

Coursework 2 description

The aim of this coursework is to further explore the classification of images of handwritten digits using neural networks. We'll be using an extended version of the MNIST database, the EMNIST dataset. Part A of the coursework will consist of building baseline deep neural networks for the EMNIST classification task, implementation and experimentation of the Adam and RMSProp learning rules, and implementation and experimentation of Batch Normalisation. Part B will concern implementation and experimentation of convolutional networks. As with the previous coursework, you will need to hand in test files generated from your code, and a report.

The code for the coursework is available on the course Github repository on a branch mlp2017-8/coursework_2. This also includes a LaTeX template (report/mlp-cw2-template.tex) for your report. Full details in the Coursework 2 document.

Note that it is in your interest to start running the experiments for this coursework as early as possible. Some of the experiments may take significant compute time.

MLP in semester 2 will be based on projects done in groups of 2-3 students. The projects, which should be done using one of the deep learning toolkits (TensorFlow is recommended) can be chosen from a variety of topics and data sets. These projects correspond to courseworks 3 and 4.

Coursework 3: Submit 16:00, 19 February 2018, Feedback 12 March 2018. Worth 25%.
Coursework 3 description
This is the interim report of your group including the the motivation and introduction to your project, the objectives, the data set and task, the methodology, the first phase of experiments, and a plan for the rest of the project.
Coursework 4: Submit 16:00, 23 March 2018, Feedback 13 April 2018. Worth 40%.
Coursework 4 description
This is the final report of the project with a focus on the main experiments, results, interpretation, related work, and conclusions.

Please make sure you have read and understood the academic misconduct policy.

Copyright (c) University of Edinburgh 2015-2018
The MLP course material is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
licence.txt
The software at https://github.com/CSTR-Edinburgh/mlpractical is licensed under the Modified BSD License.
This page maintained by Steve Renals.
Last updated: 2018/08/14 13:06:34UTC

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