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Machine Learning Practical (MLP) 2017-18



The Machine Learning Practical (MLP) for 2017-18 will be is concerned with deep neural networks. Doing this course involves the following:

During semester 1 we shall focus on the classification of handwritten digits using the well-known MNIST dataset. Using a Python software framework that we shall provide, and a series of Jupyter 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 ten weekly lectures in semester 1 to provide the required theoretical support to the practical work.

Semester 2 will be project-based, with a focus on using deep neural networks within the context of a group project, typically using TensorFlow (or another deep learning toolkit). The lectures will support the coursework, and also provide insights to the current state of the art in this very fast moving area, through some guest lectures.

Required background

The MLP course requires mathematical ability (calculus, linear algebra, and probability) and programming ability (the course will be based on Python using Numpy and TensorFlow). 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.


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The MLP course material is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
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This page maintained by Steve Renals.
Last updated: 2018/08/14 13:06:34UTC

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