Location: Academic Organisations » Stanford Engineering Everywhere » Stanford Engineering Everywhere CS229 - Machine Learning

event thumbnail image

Stanford Engineering Everywhere CS229 - Machine Learning

Lecture 11 - Bayesian Statistics and Regularization

author:Andrew Ng, Computer Science Department, Stanford University
published: May 18, 2009, recorded: April 2009, views: 771

Turn off the lights

You might be experiencing some problems with Your Video player.

See Also:

Launch Windows Media Player Launch in a standalone WM Player

WMedia Player Switch to Windows Media Player

Help icon Streaming Video Help

WebLink icon Windows Media Player Firefox Plugin - Download

Related content

Visitors who watched this lecture also watched...

Bayesian Inference: Principles and Practice
2346 views - Mike Tipping, 2003

Lecture 1 - The Motivation & Applications of Machine
909 views - Andrew Ng, 2009

Introduction To Bayesian Inference
3090 views - Christopher Bishop, 2009

Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making
8324 views - Nando de Freitas, 2008

Lecture 12 - The Concept of Unsupervised Learning
144 views - Andrew Ng, 2009

Lecture 13 - Mixture of Gaussian
136 views - Andrew Ng, 2009

Lecture 14 - The Factor Analysis Model
342 views - Andrew Ng, 2009

Lecture 10 - Uniform Convergence - The Case of Infinite H
96 views - Andrew Ng, 2009

Lecture 2 - An Application of Supervised Learning - Autonomous Deriving
323 views - Andrew Ng, 2009

Lecture 9 - Bias/variance Tradeoff
281 views - Andrew Ng, 2009

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.

Lecture popularity: You need to login to cast your vote.

Description

Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example - Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

Comment
Name
Email address
URL

make sure you have javascript enabled or clear this field: