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Machine-learning techniques, employing both classification and regression models and able to produce various outcomes, will be introduced using real world examples.

The covered methods include Naïve Bayes, Random Forest, Support Vector Machines, and Neural Networks.

The course will demonstrate how to use validation and statistical tools to assess the quality, and demonstrate the business value of a model.

Learning Outcomes:

On finishing this course, attendees will be able to:

• identify which cheminformatics problems are suitable for applying machine learning
• define problems in a way that enables a machine learning expert without chemistry knowledge to contribute to a solution
• choose and execute suitable methods for solving a problem
• evaluate a model that claims to solve a particular problem
• compare and contrast various potential models
• demonstrate the business value of a model

This is an externally organised event, more information can be found on the event organiser page here.