2023 Fall
STAT 154 001 - LEC 001
Modern Statistical Prediction and Machine Learning
Nikita Zhivotovskiy
Class #:22924
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Statistics
Current Enrollment
Total Open Seats:
2
Enrolled: 48
Waitlisted: 0
Capacity: 50
Waitlist Max: 14
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.
Final Exam
TUE, DECEMBER 12TH
03:00 pm - 06:00 pm
Cory 277
Other classes by Nikita Zhivotovskiy
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, the combination of Data/Stat C140 and Data/Stat/Compsci C100, 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
No Reserved Seats
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials