STATS315B
Modern Applied Statistics: Learning II
Statistics
H&S - Humanities & Sciences
Course Description
Modern statistical machine learning topics moving beyond linear regression and classification. Decision trees (boosting, random forests) and deep learning techniques for non-linear regression and classification tasks. Discovering patterns and low-dimensional structure via unsupervised learning, including clustering, EM algorithm, PCA and factor analysis, (variational) autoencoding methods, and matrix factorization. Time series and sequence modeling via state space models and deep learning methods (recurrent neural networks, seq2seq models, transformers). Students entering the course are assumed to have foundational working knowledge in statistics, probability, and basic machine learning concepts, though the course has been designed to provide a broadly accessible treatment of the topics covered.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Does this course satisfy the University Language Requirement?
No
Programs
STATS315B
is a
completion requirement
for:
- (from the following course set: )
- (from the following course set: )