Statistical Learning Theory and Applications - Class 7
Offered By: MITCBMM via YouTube
Course Description
Overview
Explore key concepts in statistical learning theory and applications in this comprehensive lecture. Delve into topics such as least squares, gradient descent, regularization, induction, and early stopping. Gain insights into historical facts related to the field and engage in a name game to reinforce learning. Enhance your understanding of fundamental principles and practical applications in statistical learning through this in-depth class session.
Syllabus
Intro
Least Squares
Gradient Descent
Regularization
Induction
Early stopping
Historical fact
Name game
Taught by
MITCBMM
Related Courses
An Introduction to Machine Learning in Quantitative FinanceUniversity College London via FutureLearn Art and Science of Machine Learning auf Deutsch
Google Cloud via Coursera AI skills for Engineers: Supervised Machine Learning
Delft University of Technology via edX AWS ML Engineer Associate 2.3 Refine Models (Simplified Chinese)
Amazon Web Services via AWS Skill Builder Build Regression, Classification, and Clustering Models
CertNexus via Coursera