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Machine Learning Practical (MLP) 2018-19



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 investigate neural network learning with a 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 course will explore multi-layer neural network classifiers, convolutional network classifiers, and recurrent networks. The lectures in semester 1 will provide the required theoretical support for the practical work.

Semester 2 will be based around group projects, typically using TensorFlow, PyTorch, or another deep learning toolkit. The lectures in semester 2 will cover more advanced material in deep learning.

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. 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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This page maintained by Steve Renals.
Last updated: 2019/09/16 15:42:09UTC

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