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Mon 21 Sep - Thu 24 Sep 2020
10:00, ...

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

Provided by: Bioinformatics


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Data Science School: Machine learning applications for life sciences
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Mon 21 Sep - Thu 24 Sep 2020

Description

This 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 Centre for Data-Driven Discovery (C2D3).

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 £400. All Members of the University of Cambridge to pay £200. 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 21 Sep   10:00 - 17:30 10:00 - 17:30 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Marta Milo,  Dr John C. Thomas
2 Tue 22 Sep   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Marta Milo,  Mario Guarracino
3 Wed 23 Sep   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Javier Gonzalez Hernandez
4 Thu 24 Sep   09:30 - 15:00 09:30 - 15:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Alexis Boukouvalas,  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
10:00 - 11:00 Introduction of Data Science in Life Sciences
11:00 - 12:00 Principles of experimental design
12:00 - 13:00 Lunch (provided)
13:00 - 17:00 Python recap
17:00 - 17:30 Q&A
Day 2
9:30 - 10:30 Introduction to Machine Learning for biomedical data analysis in Python
10:30 - 12:00 Data Preparation: sources of data, cleaning up your data and preparing data structure
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 - 10:30 Introduction to predictive models
10:30 - 12:00 Case studies on predictive models
12:00 - 13:00 Lunch (provided)
13:00 - 17:30 Model based experimental design, optimization - practical application with Emukit
19:00 School dinner
Day 4
9:30 - 10:30 Introduction to Latent factor models, monocle2, GPLVM
10:30 - 12:00 Implementation of a GP on scRNA-seq
12:00 - 13:00 Lunch (provided)
13:00 - 15:00 Future of AI in biomedical research
15:00 - 15:30 Q&A
Registration fees
  • All participants attending this course will be charged a registration fee.
  • Non-members of the University of Cambridge to pay 400.00 GBP
  • All Members of the University of Cambridge to pay 200.00 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

4

Related courses
Theme
Specialized Training

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