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Wed 15 Jul - Fri 17 Jul 2020
09:30 - 17:00

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

Provided by: Bioinformatics


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Bookings cannot be made on this event (Event is completed).


Other dates:


Mon 5 Oct 2020


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An Introduction to Machine Learning (ONLINE LIVE TRAINING)
Prerequisites

Wed 15 Jul - Fri 17 Jul 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.

Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.

Please be aware that the course syllabus is currently being updated following feedback from the last event; therefore the agenda below will be subjected to changes.

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
  • This is aimed at life scientists with little or no experience in machine learning and that are looking at implementing these approaches in their research.
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
  • Please be aware that these courses are only free for registered University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
  • Further details regarding eligibility criteria are available here
Prerequisites
Sessions

Number of sessions: 3

# Date Time Venue Trainers
1 Wed 15 Jul   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) Dr Manik Garg,  Dr Yasin Memari,  Dr Irina Mohorianu,  Dr Soumya Neel,  Dr Adi Steif
2 Thu 16 Jul   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) Dr Manik Garg,  Dr Yasin Memari,  Dr Irina Mohorianu
3 Fri 17 Jul   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST) Dr Yasin Memari,  Dr Christopher Penfold
Topics covered

Data mining, Machine learning

Objectives

During this course you will learn about:

  • Some of the core mathematical concepts underpinning machine learning algorithms.
  • Classification (supervised learning): partitioning data into training and test sets; feature selection; logistic regression; support vector machines; artificial neural networks; decision trees; nearest neighbours, cross-validation.
  • Exploratory data analysis (unsupervised learning): dimensionality reduction, anomaly detection, clustering.
Aims

After this course you should be able to:

  • Explain the concepts of machine learning.
  • List the strengths and limitations of the various machine learning algorithms presented in this course.
  • Select appropriate machine learning methods for your data.
  • Perform machine learning in R.
Format

Presentations, demonstrations and practicals

Timetable

This is subject to change in line with the online training schedule.

Day 1 Topics
Session 1 Machine learning and its applications in research
Session 2 Data types and partitioning
Session 3 Introduction to CARET, an R-based machine learning framework
Lunch (not provided)
Session 4 Dimensionality Reduction
Session 5 Clustering
Session 6 Review and questions
Day 2 Topics
Session 1 Nearest Neighbours
Session 2 Decision Trees and Random Forests
Session 3 Support Vector Machines
Lunch (not provided)
Session 4 Exercises on Classifiers
Session 5 Use case applying the above methods
Session 6 Review and questions
Day 3
Session 1 Linear models
Session 2 Linear and non linear logistic regression/ gaussian processes
Lunch (not provided)
Session 3 Artificial Neural Networks
Session 4 Use case applying the above methods
Session 5 Review, questions and resources for further study
Registration Fees
  • Free for registered University of Cambridge students
  • £ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
  • It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
  • £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
  • £ 100/day for all Industry participants. These charges must be paid at registration
  • Further details regarding the charging policy are available here
Duration

3

Frequency

several times a year

Related courses
Theme
Specialized Training

Booking / availability