skip to navigation skip to content
Wed 17 Jan - Thu 18 Jan 2018
09:30 - 17:00

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

Provided by: Bioinformatics


Booking
This course is full - Add me to the waiting list

Other dates:

No more events

[ Show past events ]



Register interest
Register your interest - if you cannot make any of the currently scheduled dates and would be interested in additional dates being scheduled.


Booking / availability

An Introduction to Machine Learning
PrerequisitesNew

Wed 17 Jan - Thu 18 Jan 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 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 introductory course is aimed at biologists with little or no experience in machine learning.
  • 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: 2

# Date Time Venue Trainers
1 Wed 17 Jan 2018   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Dr Sudhakaran Prabakaran,  Dr Matt Wayland,  Christopher Penfold
2 Thu 18 Jan 2018   09:30 - 17:00 09:30 - 17:00 Bioinformatics Training Room, Craik-Marshall Building, Downing Site map Dr 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 Speakers
09:30 - 11:00 Linear models and matrix algebra Prabakaran, Wayland & Penfold
11:00 - 11:15 Tea/Coffee Break
11:15 - 12:45 Linear and non linear logistic regression Prabakaran, Wayland & Penfold
12:45 - 13:30 Lunch (not provided)
13:30 - 15:00 Nearest Neighbours Prabakaran, Wayland & Penfold
15:00 - 15:15 Tea/Coffee Break
15:15 - 16:45 Decision Trees and Random Forests Prabakaran, Wayland & Penfold
16:45 - 17:00 Review and questions
Day 2
9:30 – 11:00 Support Vector Machines Prabakaran, Wayland & Penfold
11:00 - 11:15 Tea/Coffee Break
11:15 - 12:45 Artificial Neural Networks Prabakaran, Wayland & Penfold
12:45 - 13:30 Lunch (not provided)
13:30 - 15:00 Dimensionality Reduction Prabakaran, Wayland & Penfold
15:00 - 15:15 Tea/Coffee Break
15:15 - 16:45 Clustering Prabakaran, Wayland & Penfold
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 days

Frequency

A number of times per year

Related courses
Theme
Specialized Training

Booking / availability