Skip to main content
Graph
Search
fr
|
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Risk Estimation: Mallows' CL and Cp
Graph Chatbot
Related lectures (30)
Previous
Page 2 of 3
Next
Nonparametric Statistics: Bayesian Approach
Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.
Regression: Simple and Multiple Linear
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Model Building: Linear Regression
Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
Model Selection: Least Squares
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Linear Systems: Modeling and Identification
Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.
Linear Regression Basics
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Basics of Linear Regression
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Linear Regression: Least Squares
Delves into linear regression, emphasizing least squares estimation, residuals, and variance.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Kernel Methods: Understanding Overfitting and Model Selection
Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.