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Introduction to Machine Learning


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.



Dr Sudhakaran Prabakaran, University of Cambridge

Dr Matt Wayland, University of Cambridge

Dr Christopher Penfold, University of Cambridge


Audience and Prerequisites

  • This introductory course is aimed at biologists with little or no experience in machine learning.
  • Some familiarity with R would be helpful.
  • For an introduction to R see An Introduction to Solving Biological Problems with R course.
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals


Syllabus, Tools and Resources

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.


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.



Book Here

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