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Machine Learning for Health and Bioinformatics

Key Details

Date: 11th May 2020 – 15th May 2020
Time: 09:30-17:00
Venue: IoPPN Boardroom, Main Building, Institute of Psychiatry, Psychology & Neurosciences (IoPPN), King's College London. View Map.


Course Aim

This 5 day 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.
Learning Outcomes

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

  • External Early bird: £855 (till 12/03/20, price thereafter £950)
  • KCL Staff Early bird: £641.25 (till 12/03/20, price thereafter £712.50)
  • KCL Student Early bird: £427.50 (till 12/03/20, price thereafter £475)
  • Other Student Early bird: £641.25 (till 12/03/20, price thereafter £712.50)
  • Kings Health Partner Early bird: £641.25 (till 12/03/20, price thereafter £712.50)

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 / Application

Booking for this course is open

To apply please email with the following details:

Subject: Application for Machine Learning 2020


Email Address:

Contact Phone Number:

  1. Are you affiliated with KCL and/or King's Health Partners?
    1. If Yes, indicate how you are affiliated with KCL and/or King's Health Partners
    2. Indicate your education/employee status: KCL PhD, KCL student, KCL staff, King's Health Partners affiliate, External Student or External
  2. 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.

A 10% early bird discount will be available until 12th March 2020 and booking will close on 4th May 2020

Course Team

Dr Zina Ibrahim (Academic Lead)

ZinaIbrahimZina 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.

Research portal
See Zina's research portal here.

Dr Raquel Iniesta (Academic Lead)

RaquelIniestaRaquel 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.

Research interests

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.

Research portal

See Raquel's research portal here.

Dr Angus Roberts

AngusRobertsAngus is a senior lecturer in health informatics at the Department of Biostatistics and Health Informatics, King's College London.

Research portal

See Angus' research portal here.

Florian Privé, TIMC-IMAG

FlorianPrivéFlorian 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).

Research portal

See Florian's research portal here.

Cristina Venturini, University College London

CristinaVenturiniCristina is a Research Associate at the Division of Infection and Immunity, University College London.

Research portal

See Cristina's research portal here.

Conrad Iyegbe

ConradIyegbeConrad 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

Research portal

See Conrad's research portal here.

Ken Hanscombe

KenHanscombeKen is a Postdoctoral Research Associate at the Department of Medical and Molecular Genetics, King's College London.

Research portal

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

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