— Statistical Methods in Machine Learning
Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Use professional level software.
Topics
- resampling methods
- regularization techniques in regression and modern classification
- cluster analysis and dimension reduction techniques
Topics explicitly listed in official catalogue description
Prerequisites & course information
Course Description: Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Use professional level software. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Grade Mode: Letter. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Grade Mode: Letter.
Sources
- Course catalogue ↗2026-2027