Machine Learning & Applied Statistics Summer School 2019By Imperial College Business School
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.
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.
- Course dates: 1 – 19 July 2019
- Direct contact hours: 45
- Academic level: Equivalent to an undergraduate course
- Entry requirement: A level mathematics (grade A or above) or equivalent. It is desirable (but not a formal requirement) that students have some basic programming skills. You must also have successfully completed the first year of undergraduate studies in a quantitative subject such as Mathematics, Statistics, Computing, Physics, or Engineering.
- Suggested credit level: 3 – 4 US / 7.5 ECTS credits. Your home institution will determine how much credit is awarded