Machine Learning for Chemists

New

PhDs and Postdocs welcome, no prior knowledge required

Machine learning has become a common feature of many scientific papers, including chemistry, biology, and chemical biology. But what does it all mean? In this course, we will investigate the core features of common machine learning techniques such as principal component analysis (PCA), support vector machines (SVMs), and Random Forests, and how these can be applied to a real-world chemistry dataset. This course is meant to serve as a gentle introduction to machine learning - no prior knowledge is required.

Emma began her career as an experimental chemist, gaining her PhD in chemoenzymatic total synthesis under the tutelage of Prof. Hans Renata (The Scripps Research Institute, FL). During the course of her doctorate, she gained an interest in machine learning and how it can be applied to help chemists understand their systems. She thus joined the group of Dr. Alpha Lee (University of Cambridge) and later the group of Prof. Matthew Gaunt (University of Cambridge) to hone her expertise in computer programming, algorithm development, and machine learning for chemistry application.

Number of sessions: 5