Application of Data Science, Statistical Learning, and Machine Learning approaches to modern problems in Chemical and Materials Engineering. This course develops data science approaches, including their foundational mathematical and statistical basis, and applies these methods to data sets of limited size and precision. Methods for regression and clustering will be developed and applied, with an emphasis on validation and error quantification. Techniques that will be developed include linear and nonlinear regression, clustering and logistic regression, dimensionality reduction, unsupervised learning, neural networks, and hidden Markov models. These methods will be applied to a range of engineering problems, including conducting polymers, water purification membranes, battery materials, disease outcome prediction, genomic analysis, organic synthesis, and quality control in manufacturing. Undergraduates should enroll in 4 units and Graduates should enroll in 3 units.
3-4 units · Letter or Credit/No Credit
Application of Data Science, Statistical Learning, and Machine Learning approaches to modern problems in Chemical and Materials Engineering. This course develops data science approaches, including their foundational mathematical and statistical basis, and applies these methods to data sets of limited size and precision. Methods for regression and clustering will be developed and applied, with an emphasis on validation and error quantification. Techniques that will be developed include linear and nonlinear regression, clustering and logistic regression, dimensionality reduction, unsupervised learning, neural networks, and hidden Markov models. These methods will be applied to a range of engineering problems, including conducting polymers, water purification membranes, battery materials, disease outcome prediction, genomic analysis, organic synthesis, and quality control in manufacturing. Undergraduates should enroll in 4 units and Graduates should enroll in 3 units.
Offered in Winter 2027 at Stanford University.