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Thu 17 Sep, Fri 18 Sep, ... Tue 22 Sep 2020
10:00, ...

Venue: Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

Provided by: Bioinformatics


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Data Science School: Machine learning applications for life sciences (Online)
Special£

Thu 17 Sep, Fri 18 Sep, ... Tue 22 Sep 2020

Description

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout. We plan on organising opportunities for networking during this 4-day workshop and encourage attendee participation as much as possible.

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).

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 Thu 17 Sep   10:00 - 17:30 10:00 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) Marta Milo,  Dr John C. Thomas
2 Fri 18 Sep   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) Marta Milo,  Mario Guarracino
3 Mon 21 Sep   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) Javier Gonzalez Hernandez
4 Tue 22 Sep   09:30 - 15:00 09:30 - 15:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) 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

To be updated to fit an online format.

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 (not 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 (not provided)
13:00 - 17:00 Introduction to Machine Learning for biomedical data analysis in Python
17:00 - 17:30 Q&A
Day 3
9:30 - 10:30 Introduction to predictive models
10:30 - 12:00 Case studies on predictive models
12:00 - 13:00 Lunch (not provided)
13:00 - 17:30 Model based experimental design, optimization - practical application with Emukit
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 (not 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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