Spring 2019
STAT 154 001 - LEC 001
Modern Statistical Prediction and Machine Learning
Current Enrollment
Total Open Seats:
6
Enrolled: 144
Waitlisted: 0
Capacity: 150
Waitlist Max: 32
Open Reserved Seats:
148 reserved for Students with Enrollment Permission
Hours & Workload
3 hours of instructor presentation of course materials, 7 hours of outside work hours, and 2 hours of instructional experiences requiring special laboratory equipment and facilities.
Final Exam
THU, MAY 16TH
07:00 pm - 10:00 pm
Li Ka Shing 245
Other classes by Bin Yu
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:
148 reserved for Students with Enrollment Permission