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Courses offered

Machine Learning for Health and Bioinformatics

Academic Leads: Dr Zina Ibrahim and Dr Raquel Iniesta

Instructors: Angus Roberts (KCL), Florian Privé (TIMC-IMAG, Grenoble), Zina Ibrahim (KCL), Cristina Venturini (UCL), Conrad Iyegbe (KCL), Ken Hanscombe (KCL), Raquel Iniesta (KCL)

Date: 20th May 2019 – 22nd May 2019

Time: 09:30-17:00

Venue: Computer Rooms A & B, Main Building. Institute of Psychiatry, Psychology and Neurosciences (IoPPN), King's College London. View Map.

Cost:

  • 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)

Last booking: 20th May 2019

BOOKING: For more information and to book visit this link.


 

COURSE AIM

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.

LEARNING OUTCOME

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.


GENERAL: Knowledge, Understanding and Skills.

On successful completion of this module the student should be able to:

  • Become capable to solve specific data problems applying a systematic approach.
  • Communicate clearly, concisely and correctly the output of performing a machine learning analysis in the written and spoken form. 
  • Use a variety of thinking skills to anticipate and solve problems using machine learning.
  • Interact with others in groups or teams in ways that contribute to effective working relationships and the achievement of goals in data analysis.
  • Manage the use of time and other resources to complete projects involving machine learning approaches of analysis.

 

Application: To apply please email us (iop-biostatisticseducation@kcl.ac.uk) with the following details:

Email Subject Line: Application for Machine Learning 2019

Name:

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.


 

Please note the following:

If you would like to pay by internal transfer, please contact iop-biostatisticseducation@kcl.ac.uk

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.

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