2017 Fall
STAT 154 001 - LEC 001
Modern Statistical Prediction and Machine Learning
Current Enrollment
Total Open Seats:
0
Enrolled: 74
Waitlisted: 2
Capacity: 74
Waitlist Max: 2
No Reserved Seats
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.
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 and 54 or equivalents; Statistics 135 or equivalent; experience with some programming language. Mathematics 55 or equivalent exposure to counting arguments is recommended but not required.
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats
Textbooks & Materials
Textbook information is not available for Fall 2017.