Machine Learning and Applications in Chemistry

CHEM C225 - Machine Learning and Applications in Chemistry

<p>(Formerly numbered 125.) Lecture, three hours; computer laboratory, one hour. Requisite: course 14C or 30A, with grade of C– or better. Comprehensive introduction to machine learning (ML) algorithms and their applications in the field of chemistry and biochemistry. Study is designed to provide students with both the theoretical understanding of ML algorithms and the practical skills needed to apply these techniques to solve real-world problems in chemistry. Through lectures, hands-on laboratories, and project work, students learn to analyze chemical datasets, predict chemical properties, and develop models for chemical behavior using ML. Concurrently scheduled with course C225. P/NP or letter grading.</p>

Chemistry and Biochemistry

College of Letters and Science

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CHEM C125 - Machine Learning and Applications in Chemistry

Use regression, classification, and clustering methods to predict chemical properties and classify chemical compounds.

Understand the fundamental principles and algorithms of machine learning.

Evaluate the performance of machine learning models using appropriate metrics.

Apply various machine learning techniques, including supervised and unsupervised learning, to chemical data analysis.

Demonstrate the ability to conduct a machine learning project from data preprocessing to model development and evaluation in a chemical context.

Implement neural networks and deep learning models for complex problem-solving in chemistry.

Utilize machine learning software and tools relevant to chemistry and biochemistry research.

General Catalog

Machine Learning and Applications in Chemistry

CHEM C125

4 units