Probabilistic Machine Learning for Scientific Discovery

3.0 credits · Graduate

As machine learning is increasingly applied in science and engineering, there is a growing need for methods that can model complex data assumptions, quantify uncertainty, and make reliable predictions and decisions. This course covers foundational and recent developments in probabilistic machine learning, including generative modeling, uncertainty quantification, sampling, and optimization. We will also survey research papers and their applications to scientific domains, such as chemistry and physics. The course culminates in a research project inspired by a scientific application of probabilistic machine learning.