Master of Science in Machine Learning and Data Science
Imperial College London
About Imperial College London
Imperial College London is the UK’s only university to focus solely on science, engineering, medicine, and business. Consistently ranked amongst the top 10 universities in the world, Imperial is home to a global community of scientists, engineers, medics, and business experts.
Their research-led approach shapes the way Imperial College educate students through teaching that opens everything up to question. It’s a style of learning that relies on learning by discovery and prepares graduates to bring fresh perspectives to the ever-evolving landscape of technology.
- Top 10 universities in the world (QS World University Rankings 2021)
- #1-ranked UK university for “world-leading” research (REF 2021)
- University of the Year 2022 (The Times and Sunday Times Good University Guide)
- 1st in the UK for most innovative university (Reuters)
- #1 in Graduate Employability Ranking (Guardian University Guide 2021 and the Time Good University Guide 2021)
About the Department
The Department of Mathematics at Imperial College London is an internationally renowned department within one of the world's most prestigious universities. With an outstanding level of teaching and research, their principal aim is to train professional mathematicians and statisticians to pursue the study of scientific and technological problems using mathematical methods, and to undertake research in various branches of the subject.
There is great demand, both in research and in industry, for well-trained, highly qualified mathematicians and statisticians. Imperial College teaching and research programmes adapt and develop to meet this, aiming to give our students the skills they need to pursue the career of their choice.
- Top 10 university in the mathematics and statistics ranking (Times Higher Education World Rankings 2020).
- Imperial is home to world-famous mathematicians, including three winners of the Fields Medal, which recognizes outstanding mathematics achievement.
Nicholas HeardChair in Statistics, Imperial College London
Nicholas Heard received the PhD degree from the Department of Mathematics at Imperial College London (ICL) in 2001 and currently holds a chair in statistics at ICL. His research interests include developing statistical methods for cyber-security, finding community structure in large dynamic networks, clustering and changepoint analysis, meta-analysis and computational Bayesian inference. Nick will be teaching Bayesian Methods in the second year of the programme.
Niall AdamsProfessor of Statistics and Head of Statistics, Imperial College London
Niall Adams is Professor of Statistics, and Head of the Statistics section in the Department of Mathematics, Imperial College London. His research interests are focussed on anomaly detection and streaming data methods with application to areas such as cyber-security. He is the author of more than 80 refereed papers, and editor of 10 books. In the period 2011-2016, he leads the data science team at the Heilbronn Institute for Mathematical Research at the University of Bristol. Professor Adams will lead Programming for Data Science in term 1.
Christoforos AnagnostopoulosHead of Research, Improbable (Enterprise) & Honorary Senior Lecturer in Statistics, Imperial College London
Christoforos Anagnostopoulos is an Honorary Senior Lecturer in Statistics in the Department of Mathematics in Imperial College London, with a particular focus on probabilistic programming, graphical modelling and streaming data analysis. His research has been applied to a number of domains, ranging from cybersecurity to neuroimaging, and he has been working as an industrial R&D lead for several years in AI programmes for large enterprises. Christoforos will teach Ethics in Data Science and Artificial Intelligence in the programme.
Marina EvangelouSenior Lecturer in Statistics, Imperial College London
Marina Evangelou is a Senior Lecturer in Statistics in the Statistics Section of the Department of Mathematics. Throughout her career she has been interested in the development of statistical methods for the analysis of high dimensional and complex datasets from the fields of biology, health and medicine. Other interests include the modelling of cyber-security data-sources for the development of anomaly detection techniques. Dr. Evangelou will teach Unsupervised Learning in the second year.
Kevin WebsterSenior Teaching Fellow, Imperial College London
Kevin Webster is a Senior Teaching Fellow in the department of Mathematics at Imperial College London. His research interests are in the areas of machine learning, deep learning, dynamical systems, statistical learning theory and music information retrieval. Kevin Webster will be teaching Deep Learning during term 4 of the programme.
Emma McCoyProfessor of Statistics, Imperial College London
Emma McCoy is a Professor of Statistics at Imperial College London and a Strategic Leader for the 'Monitoring of complex systems' Grand Challenge of the Turing's programme in Data-centric Engineering. Emma’s research interests include time series, wavelets and causal inference, with a particular interest in transport studies. Emma will be teaching Exploratory Data Analytics and Visualisation in term 2.
Mark BriersStrategic Programme Director, Alan Turing Institute & Honorary Senior Lecturer, Imperial College London
Mark Briers is Programme Director for The Alan Turing. Prior to joining Turing, Mark worked in the defence and security sector for over 16 years, directing research programmes in the area of statistical data analysis, and leading large teams to drive impactful research outputs. He is an Honorary Senior Lecturer at Imperial College London, where he teaches methodological techniques for use in a Big Data environment and conducts research into statistical methods for cyber security, and he is a Council Member at the Royal Statistical Society. He is an Industrial Fellow alumnus of the Royal Commission for the Exhibition of 1851. Mark will teach Big Data: Scalability in term 3.
Chris HallsworthSenior Teaching Fellow, Imperial College London
Chris joined Imperial in September 2017 as Senior Teaching Fellow in Statistics. He is an applied statistician, with experience in a variety of problem domains, including genomics, continuous health monitoring and randomized controlled trials. Areas of statistical interest include time-structured data, smoothing models and neural networks. Chris is an active member of the Royal Statistical Society, sitting on the committees of the Business and Industry section and the Teaching Statistics special interest group.
James MartinSenior Teaching Fellow, Imperial College London
James Martin is a Senior Teaching Fellow in the Department of Mathematics at Imperial College London. His research interests include the use of machine learning techniques in engineering problems, the use of spatial statistics in ecological applications and the development of likelihood-free approaches to Bayesian inference. James will be teaching the Applicable Maths course in Term 1.
Anthea MonodLecturer, Imperial College London
Anthea Monod is a Lecturer in Biomathematics at the Department of Mathematics at Imperial College London. Her research entails developing algebraic methods for handling structurally complex and nonstandard data arising from biology; she works in topological data analysis and algebraic statistics. Anthea will be teaching Unstructured Data Analysis in the second year.
Ciara Pike-BurkeLecturer, Imperial College London
Ciara Pike-Burke is a Lecturer in Statistics in the Department of Mathematics at Imperial College London. Her research is in the field of statistical machine learning with a focus on sequential decision making problems. In particular, she has worked on multi-armed bandit, online learning, and reinforcement learning problems. Ciara will be teaching the Learning Agents module in the second year.
Zak VartyTeaching Fellow, Imperial College London
Zak Varty is a Teaching Fellow in Statistics and Imperial College London. His research focuses on developing novel methodology for industrial and environmental applications of statistics. This work often uses techniques from extreme value theory, point process modelling and spatial statistics. Zak teaches two modules: Ethics in Data Science and Artificial Intelligence and Supervised Learning.
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