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Mon 23 Sep - Thu 26 Sep 2019
11:30, ...

Venue: Bioinformatics Training Room, Craik-Marshall Building, Downing Site

Provided by: Bioinformatics


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Autumn School in Data Science: Machine learning applications for life sciences
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Mon 23 Sep - Thu 26 Sep 2019

Description

THIS EVENT IS NOW FULLY BOOKED!

This Autumn School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.

Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.

This event is sponsored by Cambridge Big Data.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Target audience
  • Students and researchers from life-sciences or biomedical backgrounds, who have, or will shortly have, the need to apply the techniques presented during the course to biomedical data.
  • The course is open to Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
  • Please note that all participants attending this course will be charged a registration fee. Non-members of the University of Cambridge to pay £350. All Members of the University of Cambridge to pay £175. A booking will only be approved and confirmed once the fee has been paid in full.
  • Further details regarding eligibility criteria are available here
Prerequisites
Sessions

Number of sessions: 4

# Date Time Venue Trainers
1 Mon 23 Sep   11:30 - 17:30 11:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Marta Milo,  Catherine Leroy
2 Tue 24 Sep   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Marta Milo,  Adriano Barbosa
3 Wed 25 Sep   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Adriano Barbosa,  Alexis Boukouvalas
4 Thu 26 Sep   09:30 - 15:00 09:30 - 15:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Javier Gonzalez Hernandez,  Neil Lawrence
Topics covered

Bioinformatics, Data handling, Machine learning

Objectives

After this course you should be able to:

  • Identify optimal machine learning methodologies for data analysis
  • Apply principles of experimental design to your research project
  • Visualise data and apply dimensionality reduction/clustering
  • Evaluate the use of Gaussian processes in life science applications
Aims

During this course you will learn about:

  • Introduction to Data Science and the role of Machine Learning in this field
  • Principles of experimental design and impact on downstream data analysis
  • Data readiness and its implications in collating, processing and curating data
  • Reproducible machine learning workflows
  • Learning methods for modelling biomedical data, including Gaussian Processes and latent factors models
  • Effective data visualisation and interpretation
Format

Presentations, demonstrations, and practicals

Timetable

Day 1
11:30 - 12:00 Arrival and registration
12:00 - 13:00 Lunch (provided)
13:00 - 14:00 Introduction of Data Science and Machine Learning in Life Sciences
14:00 - 15:00 Principles of experimental design
15:00 - 17:00 Python recap
17:00 - 17:30 Q&A
Day 2
9:30 - 10:30 Data Preparation: sources of data, cleaning up your data and preparing data structure
10:30 - 12:00 Introduction to Machine Learning for biomedical data analysis in Python
12:00 - 13:00 Lunch (provided)
13:00 - 17:00 Introduction to Machine Learning for biomedical data analysis in Python
17:00 - 17:30 Q&A
18:00 Pub quiz
Day 3
9:30 - 11:00 Introduction to Machine Learning for biomedical data analysis in Python
11:00 - 12:00 Building reproducible workflows
12:00 - 13:00 Lunch (provided)
13:00 - 16:00 Introduction to Gaussian processes (GP) and Latent factor models
16:00 - 17:30 Implementation of a GP on scRNA-seq
19:00 School dinner
Day 4
9:30 - 12:00 Model based experimental design, optimization - practical application with Emukit
12:00 - 13:00 Lunch (provided)
13:00 - 14:00 Future of AI in biomedical research
14:00 - 15:00 Q&A
Registration fees
  • All participants attending this course will be charged a registration fee.
  • Non-members of the University of Cambridge to pay 350.00 GBP
  • All Members of the University of Cambridge to pay 175.00 GBP.
  • A booking will only be approved and confirmed once the fee has been paid in full.
  • Further details regarding the charging policy are available here
Duration

3.5

Related courses
Theme
Specialized Training

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