An Introduction to Machine Learning

PrerequisitesNew

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.

• 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

• Some familiarity with R would be helpful.
• For an introduction to R see An Introduction to Solving Biological Problems with R course.

Number of sessions: 2

Bioinformatics, Data mining, Machine learning

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.

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.

Presentations, demonstrations and practicals

• 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

2

A number of times per year

• An Introduction to Solving Biological Problems with R