Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

ABSTRACT: Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary fiel

SUBMITTER: Remington JM 

PROVIDER: S-EPMC8323870 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature