Course Schedule and Catalog
Course Schedule - Fall Semester 2023
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
CEVE 543 001 (CRN: 15278)
DATA-DRIVEN CLIMATE HAZARD
Long Title: DATA-DRIVEN MODELS FOR CLIMATE HAZARD
Department: Civil & Environmental Engr
Instructor: Doss-Gollin, James W.
Meeting: 11:00AM - 11:50AM MWF (21-AUG-2023 - 1-DEC-2023)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 20
Section Enrolled: 14
Enrollment data as of: 27-JUL-2024 2:19AM
Final Exam: GR Course-Dept Schedules Exam
Description: This course covers the use of tools from data science (statistics, machine learning, and programming) to model climate hazards such as floods and droughts. Through hands-on programming assignments based on state-of-the-art published research, students will learn to apply methods to real-world problems with a strong emphasis on probabilistic methods and uncertainty quantification. Examples of potential topics covered include nonparametric statistics, convolutional neural networks, Gaussian processes, wavelets and spectral analysis, extreme value distributions, hierarchical models, and graph neural networks. Recommended Prerequisite(s): Prior coursework in Bayesian statistics (e.g., STAT 425/525) and/or machine learning (e.g., ELEC 478/578) and some comfort writing code.