EARTH 468 - Data Analysis, Inference, and Estimation
Fall 2022, Section 001
Instruction Mode: Section 001 is  In Person (see other Sections below)
Subject: Earth and Environmental Sciences (EARTH)
Department: LSA Earth & Environmental Sciences
See additional student enrollment and course instructor information to guide you in your decision making.


Requirements & Distribution:
BS, QR/2
Enforced Prerequisites:
MATH 115 or graduate standing.
Advisory Prerequisites:
Knowledge of, or willingness to learn, a programming language (e.g., Matlab, Mathematica).
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May not be repeated for credit.
Primary Instructor:


This course introduces science students to modern methods in machine learning, including statistical data analysis techniques and probabilistic methods to infer and estimate indirect information. Emphasis is placed on conceptual understanding and practical application, with material presented within a Bayesian framework. Topics include error propagation, cluster analysis, Markov Chain Monte Carlo, least squares regression, robust regression, Kalman filtering, Kriging/Gaussian process regression, and model selection.

The class will use the Python programming language for all labs and most homework. No prior experience with Python is required, and coding instruction will be integrated into all labs. Grade will be determined based on homework, hands-on computer exercises, and a course project.

Intended Audience:

Upper level science undergraduate or graduate students students.

Class Format:

In-person lectures will be recorded. Remote participation in all labs is possible.


EARTH 468 - Data Analysis, Inference, and Estimation
Schedule Listing
001 (LEC)
 In Person
MW 1:00PM - 2:00PM
002 (LAB)
M 4:00PM - 6:00PM
003 (LAB)
W 4:00PM - 6:00PM

Textbooks/Other Materials

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Syllabi are available to current LSA students. IMPORTANT: These syllabi are provided to give students a general idea about the courses, as offered by LSA departments and programs in prior academic terms. The syllabi do not necessarily reflect the assignments, sequence of course materials, and/or course expectations that the faculty and departments/programs have for these same courses in the current and/or future terms.

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CourseProfile (Atlas)

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CourseProfile (Atlas)