CEE 554 - Data Mining in Transportation
Section: 001
Term: FA 2018
Subject: Civil & Environmental Engin (CEE)
Department: CoE Civil and Environmental Engineering
3 (Non-LSA credit).
Requirements & Distribution:
Waitlist Capacity:
Enforced Prerequisites:
CEE 450; (C>) or grad
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:

Topics in predictive data analytics where computers are enabled to learn hidden structures from data without being explicitly programmed. The focus of the course is on supervised (classification and regression) and unsupervised (clustering) learning methods. Examples are drawn from the field of transportation system.

CEE 554 - Data Mining in Transportation
Schedule Listing
001 (LEC)
TuTh 1:00PM - 2:30PM
NOTE: Data maintained by department in Wolverine Access. If no textbooks are listed below, check with the department.

Bayesian Reasoning and Machine Learning and Machine learning : a probabilistic perspective are both available online.
ISBN: 9781600490064
Learning from data : a short course, Author: Abu-Mostafa, Yaser S., Publisher: AMLbook op.
ISBN: 0521518148
Bayesian Reasoning and Machine Learning, Author: Barber, David., Publisher: University Cambridge Press 2012
ISBN: 0262018020
Machine learning : a probabilistic perspective, Author: Murphy, Kevin P., 1970-, Publisher: MIT Press 2013
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.

No Syllabi are on file for CEE 554. Click the button below to search for a different syllabus (UM login required)

Search for Syllabus
The CourseProfile (ART) system, supported by the U-M Provost’s 3rd Century Initiative through a grant to the Office of Academic Innovation, provides additional information about: course enrollments; academic terms and instructors; student academic profiles (school/college, majors), and previous, concurrent, and subsequent course enrollments.

CourseProfile (ART)