Machine Learning for Health and Bioinformatics
Date: 20th May 2019 – 22nd May 2019
Venue: Seminar Rooms 1 & 2, Main Building, Institute of Psychiatry, Psychology & Neurosciences (IoPPN), King's College London. View Map.
These 3-days course will give a complete introduction to machine learning use in the complex world of health informatics and bioinformatics.
The course will cover the use of advanced techniques of predictive modelling and statistical learning (as polygenic risk scoring and regularised methods) for analysing genetics data, an introduction to health informatics to learn how to manage and use patients health information, and will also have room for methods on applied Machine Learning, where state-of-the-art algorithms, as Neural Networks and deep learning models, will be introduced and applied to problems in the domain.
The course will combine the use of R and Python, two of the most common languages for Machine Learning and Health Informatics.
This workshop will assume that participants have a basic knowledge of the syntax based statistical software, R. Participants will need to bring their own laptop computer with R installed.
At the end of the course the students should be able to demonstrate subject-specific knowledge, understanding and skills and have the ability to:
- Develop an understanding of the core Machine Learning concepts.
- Design and plan a machine learning analysis, including the necessary steps from data pre-processing to model validation.
- Asses and compare different machine learning algorithms, to identify the most suitable for the analysis of a given real data set.
- Interpret, justify, and critically discuss the outcome of using machine learning to specific data problems.
Cost and Booking
Booking / Application
- External Early bird: £405 (till 02/04/19, price thereafter £450)
- KCL Staff Early bird: £303.75 (till 02/04/19, price thereafter £337.5)
- KCL Student Early bird: £202.5 (till 02/04/19, price thereafter £225)
- Other student Early bird: £303.75 (till 02/04/19, price thereafter £337.5)
- King's Health Partners Early bird: £303.75 (till 02/04/19, price thereafter £337.5)
That is, 50% discount to King's College London PhD students, 25% discount to other students and staff at King's College London and King's Health Partners.
Booking for this course has now closed.
To apply please email email@example.com with the following details:
Subject: Application for Machine Learning 2019
Contact Phone Number:
- Are you affiliated with KCL and/or King's Health Partners?
- If Yes, indicate how you are affiliated with KCL and/or King's Health Partners
- Indicate your education/employee status: KCL PhD, KCL student, KCL staff, King's Health Partners affiliate, External Student or External
In 100 words, state which skills you hope to acquire:
Once your application has been approved, you will be sent a link to payment and a discount code if one is to be applied.
Dr Zina Ibrahim (Academic Lead)
Dr Raquel Iniesta (Academic Lead)
Zina is a Lecturer in Computer Science for Health Informatics. Her research spans theoretical foundations of knowledge representation, temporal reasoning, machine learning, and multi-agent systems, and their application in supporting biological knowledge discovery, healthcare delivery, and e-health infrastructures.
Consolidated by her experience in translational research in collaborating with healthcare practitioners, her current focus is on the conception and realisation of use cases of the learning healthcare system.
See Zina's research portal here.
Dr Angus Roberts
Raquel is a BRC Lecturer in statistical learning and precision medicine. Her main research is focused on identifying clinical and genetic predictors of risk to complex disorders and response to treatment.
She has been doing research in big data analysis and personalised medicine since 2003. After getting graduates in mathematics and statistics by the Autònoma University of Barcelona, she got a PhD in Biomedical research by the Catalan Institute of Oncology in 2010. Her activity since then has also included consultancy and teaching.
Computational statistics & machine learning; High-dimensional data modelling; Bioinformatics; Genetics and Pharmacogenetics of complex diseases (Cancer, Schizophrenia, Major Depression, Hypertension).
She has also designed a website for Statistical Learning & Prediction Modelling Research Group.
See Raquel's research portal here.
Florian Privé, TIMC-IMAG
Angus is a senior lecturer in health informatics at the Department of Biostatistics and Health Informatics, King's College London.
See Angus' research portal here.
Cristina Venturini, University College London
Florian is a Data scientist PhD student in predictive human genetics at Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG).
See Florian's research portal here.
Cristina is a Research Associate at the Division of Infection and Immunity, University College London.
See Cristina's research portal here.
Conrad began his scientific career in industry, as part of a small start-up biotech company developing novel neural transplantation therapies for Stroke. The experience ignited his passion for academic research so he undertook further studies. These culminated in a PhD exploring the transmission of prions (the infectious proteins responsible for Mad Cow's disease and its human equivalent) using molecular and quantitative methods.
His post-doctoral career has been dedicated to psychiatry research. He has a broad portfolio of current research interests that spans genetics, social science and statistics.
He also leads an international initiative (launched in 2017) whose goal is to strengthen the evidence base and advance the delivery of personalised medicine in under-researched populations
See Conrad's research portal here.
Ken is a Postdoctoral Research Associate at the Department of Medical and Molecular Genetics, King's College London.
See Ken's research portal here.
Your place will not be confirmed until payment has been made. Failure to cancel without sufficient notice will forfeit your course fee and access to future courses. If you would like to pay by internal transfer, please contact firstname.lastname@example.org