You will have an introduction to the disorders of consciousness, epilepsy and stroke. The topics include neurosurgical aspects, ischaemic vascular disease, haemorrhagic vascular disease, transient loss of consciousness and epilepsy, coma and sleep, neuro-rehabilitation. Learning is through online interactive lectures and will be facilitated through tutor and student interactions through the online forums.

On completion of the module, you will be able to apply the knowledge to form a differential diagnosis and plan management for stroke and common epilepsies. You will be able to better diagnose patients presenting with disorders of consciousness.

Machine Learning|Deep Learning|Python|Supervised Learning|Unsupervised Learning|Reinforcement L

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...

Advanced Machine Learning for Healthcare (CHME0035)

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
	
	
	Understand how does reinforcement learning work, and how to implement it in practice
	
	
	Understand the mathematics necessary for constructing ML solutions.
	
	
	Work with a range of dataset, e.g. labeled 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, François Chollet