This course is the next step beyond our introductory machine learning course and teaches students about modern techniques for machine learning with high-dimensional image and sequence (time-series) data, and the underlying computational structures for such systems.

TBC

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Exam 60%, Lab Reports 16%, Written Assignment 14%, Set Exercise 10%.

The aim of this course is to go beyond our introductory machine learning course, and teach students about modern techniques for machine learning with high-dimensional image and sequence (time-series) data, and the underlying computational structures for such systems. Teach the students about managing large data sets, and the engineering pipelines for large-scale machine learning tasks.  In this course, students will learn the foundations of deep learning and dynamic models for time-series analysis.

By the end of this course students will be able to:

1. Understand the major technology trends in advanced machine learning;

2. Build, train and apply fully connected deep neural networks;

3. Know how to implement efficient, vectorised neural networks in python and understand the underlying backends;

4. Apply deep learning methods to new applications;

5. Understand the machine learning pipeline, and engineering aspects of training data collation, and the importance of unlabelled data.