Statistical Learning - Advanced Regression

Subject: 
Statistics (STAT)
Catalog number: 
844
Unit weight: 
0.50
Meet type: 
LEC
Grading basis: 
NUM
Cross-listing(s): 
CM-764
Requisites: 
Antireq: CM 464STAT 444
Description: 
This course introduces modern applied regression methods for continuous response modelling, emphasizing both explainability and predictive power. Topics cover a wide selection of advanced methods useful to address the challenges arising from real-world and high-dimensional data; methods include robust regression, nonparametric regression such as smoothing splines, kernels, additive models, tree based methods, boosting and bagging, and penalized linear regression methods such as the ridge regression, lasso, and their variants. Students will gain an appreciation of the mathematical and statistical concepts underlying the methods and also computational experience in applying the methods to real data.
Topic titles: 
N/A
Faculty: 
Mathematics (MAT)
Academic level: 
GRD
Course ID: 
003092