Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization
APMTH 207
Subject & Catalog Number
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Course Information
Description
The class aims to highlight the process of scientific discovery under uncertainty in the age of data. The class content stresses a unifying approach to data driven modeling and inference through stochastic simulations, optimization and Bayesian uncertainty quantification. The class projects require transferring an idea to software in multi- and many-core computer architectures.
School
Faculty of Arts & Sciences
Credits
4
Cross Reg
Available for Harvard Cross Registration
Department
Applied Mathematics
Course Component
Lecture
NOTE: This course requires additional sections; you will be prompted to choose secondary components during the Add to Cart process
General Education
N/A
Quantitative Reasoning with Data
Yes
Divisional Distribution
Science & Engineering & Applied Science
Course Level
Primarily for Graduate Students
