Explore the fundamental concepts of statistical learning theory and applications in this comprehensive lecture from MIT's course on the subject. Delve into topics such as initial guesses, Newton's method, recursive computation, residuals, rank 1 updates, and online methods. Gain a deeper understanding of these essential techniques and their practical applications in statistical learning and data analysis.
Statistical Learning Theory and Applications - Class 6
Overview
Syllabus
Intro
Initial guess
Newton method
Recursive computation
Newtons method
Residuals
Rank 1 Update
Online Method
Taught by
MITCBMM
Related Courses
-
Statistical Learning Theory and Applications - Class 7
-
Statistical Learning Theory and Applications - Class 16
-
Statistical Machine Learning
5.0 -
Stochastic Gradient Descent and Machine Learning - Lecture 1
-
Overview of Statistical Learning Theory - Part 2
-
Statistical Learning Theory and Neural Networks II
