Gain data analytics skills and apply them to challenging problems in finance and bioimaging
Machine Learning & Applied Statistics Summer School will introduce you to a range of quantitative methods from mathematics, statistics and computing and will enable you to use these methods in applications in various fields including finance and bioimaging.
This new course is offered jointly by the Department of Mathematics and Imperial College Business School and facilitated by the Quantitative Sciences Research Institute. You will be taught by faculty from the Department of Mathematics.
During the course you will:
Acquire basic programming skills
Gain familiarity with and experience in implementing classic machine learning algorithms for supervised and unsupervised learning
Develop your understanding of the key concepts of deep learning
Learn how to fit suitable models to time series data, to assess the model fit and to make predictions
Gain experience in processing real bioimaging data
Code algorithms for performing statistical tests and correctly interpret the results
Further information about Machine Learning & Applied Statistics is available in the course outline.
The course will be delivered by a mix of face-to-face lectures and classes. Lecture content and class material will be made available through an interactive online teaching and learning hub – The Summer School Hub.
Case studies, in-class computing demonstrations and exercises will be used to link the theoretical concepts you learn to applications. You will also be expected to complete significant private study and exam preparation outside of your scheduled classes.
One individual examination at the end of the first week – (33.33% of final mark)
One individual final examination at the end of the third week – (66.66% of final mark)
Imperial College London will issue an official transcript with a final overall numerical mark – a breakdown of results will not be provided.
Imperial College London reserves the right to change or alter the courses offered without notice.