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Wed 26 Sep - Fri 28 Sep 2018
13:30, ...

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

Provided by: Bioinformatics


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


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An Introduction to Machine Learning
Prerequisites

Wed 26 Sep - Fri 28 Sep 2018

Description

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 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 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 26 Sep 2018   13:30 - 17:00 13:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Sudhakaran Prabakaran,  Dr Matt Wayland,  Christopher Penfold
2 Thu 27 Sep 2018   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Sudhakaran Prabakaran,  Dr Matt Wayland,  Christopher Penfold
3 Fri 28 Sep 2018   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Sudhakaran Prabakaran,  Dr Matt Wayland,  Christopher Penfold
Topics covered

Bioinformatics, Data mining, Machine learning

Objectives

After this course you should be able to:

  • Understand the concepts of machine learning.
  • Understand 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.
Aims

During this course you will learn about:

  • Some of the core mathematical concepts underpinning machine learning algorithms: matrices and linear algebra; Bayes' theorem.
  • 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.
Format

Presentations, demonstrations and practicals

Timetable

Day 1 Topics
13:30 - 14:30 Introduction to general machine learning
14:30 - 15:30 Introduction to CARET, an R-based machine learning framework
15:30 - 16:30 Introduction to statistical machine learning
16:30 - 17:00 Q&A
Day 2 Topics
09:30 - 11:00 Linear models and matrix algebra
11:00 - 11:15 Tea/Coffee Break
11:15 - 12:45 Linear and non linear logistic regression
12:45 - 13:30 Lunch (not provided)
13:30 - 15:00 Nearest Neighbours
15:00 - 15:15 Tea/Coffee Break
15:15 - 16:45 Decision Trees and Random Forests
16:45 - 17:00 Review and questions
Day 3
9:30 – 11:00 Support Vector Machines
11:00 - 11:15 Tea/Coffee Break
11:15 - 12:45 Artificial Neural Networks
12:45 - 13:30 Lunch (not provided)
13:30 - 15:00 Dimensionality Reduction
15:00 - 15:15 Tea/Coffee Break
15:15 - 16:45 Clustering
16:45 - 17:00 Review, questions and resources for further study
Registration Fees
  • Free for University of Cambridge students
  • £ 50/day for all University of Cambridge staff, including postdocs, 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

2.5

Frequency

2 times a year

Related courses
Theme
Specialized Training

Booking / availability