An Introduction to Machine Learning (ONLINE LIVE TRAINING)

Prerequisites

THIS COURSE IS NOT RETURNING IN ITS CURRENT FORM. PLEASE CHECK OUR WEBSITE FOR MORE INFORMATION.

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 or register your interest by linking here.

• 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.
• After you have booked a place, if you are unable to attend any of the live sessions and would like to work in your own time, please email the Bioinfo Team as Attendance will be taken on all courses. A charge is applied for non-attendance, including for registered university students.
• Further details regarding eligibility criteria are available here

• Participants should be experienced in programming in R as the course will build on this. We recommend the Introduction to R for biologists course as a first course to start programming in R. If you are not able to attend an introductory course, please work through the R material as a minimum.

Data mining, Machine learning

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.

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.

Presentations, demonstrations and practicals

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

• 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

3

several times a year

• Introduction to R for Biologists (IN-PERSON)