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Instructor-led course

Provided by: Department of Chemistry


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Chemistry: Machine Learning in Chemistry 101
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Description

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Target audience
  • Chemistry postgraduates
  • Further details regarding eligibility criteria are available
Topics covered
  • Session 1: Basics in Probabilities, Statistics and Machine Learning + Workshop
  • Session 2: Regression, Classification and Prediction Part 1
  • Session 3: Regression, Classification and Prediction Part 2
  • Session 4: Guest Lecture
  • Session 5: Dimensional Reduction and Data Visualization + Workshop
  • Session 6: Making the Connection between Machine Learning and Chemistry Part 1
  • Session 7: Making the Connection between Machine Learning and Chemistry Part 2 + Optional Workshop
  • Session 8: Guest Lecture
  • Session 9: Guest Lecture
Bibliography
Theme
Machine Learning

Events available