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Description
In this course, with basic knowledge of machine learning (ML) you will learn in-depth ML algorithms and data analysis focusing on recent techniques, such as deep learning with neural networks (convolutional and recurrent neural networks). Methods in supervised learning, unsupervised learning and reinforcement learning are introduced in the course as well as practical techniques and tips to effectively build and maintain codes for various applications. This is an intensive, practical course for people wanting to gain hands-on experience of modelling and analytic ML techniques.
By the end of the module you should be able to:
- Be familiar with widely-used ML algorithms
- Create structured code for practical implementation and reproducible research
- Know the general approaches to optimize the models
- Understand how to build advanced ML models to solve supervised and unsupervised learning problem in clinical environments
- Understand how does reinforcement learning work, and how to implement it in practice
- The foundation of NLP and language model, and their application in healthcare area
- Understand the mathematics necessary for constructing ML solutions.
- Work with a range of dataset, e.g. labelled data, clinical data, time series data, etc.
- Be able to design and implement various ML algorithms in a range of real-world applications.
You will learn though a combination of lectures, invited speakers, problem classes and group work.
Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville
Deep Learning with Python, Fran莽ois Chollet
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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