Spring 2020
STAT 154 001 - LEC 001
Modern Statistical Prediction and Machine Learning
Current Enrollment
Total Open Seats:
23
Enrolled: 117
Waitlisted: 0
Capacity: 140
Waitlist Max: 32
Open Reserved Seats:
19 unreserved seats
4 reserved for Statistics Graduate: Masters Students
Hours & Workload
3 hours of instructor presentation of course materials per week, 7 hours of outside work hours per week, and 2 hours of instructional experiences requiring special laboratory equipment and facilities per week.
Final Exam
THU, MAY 14TH
03:00 pm - 06:00 pm
Internet/Online
Other classes by Gaston Sanchez Trujillo
Course Catalog Description
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.
Rules & Requirements
Requisites
- Mathematics 53 or equivalent; Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics 110 or equivalent linear algebra; Statistics 135 or equivalent; experience with some programming language. Recommended prerequisite: Mathematics 55 or equivalent exposure to counting arguments.
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
Open Reserved Seats:
19 unreserved seats
4 reserved for Statistics Graduate: Masters Students