Lecture 9: Understanding Experimental Data

Update: 2017-05-10

Description

Prof. Grimson talks about how to model experimental data in a way that gives a sense of the underlying mechanism and to predict behavior in new settings.

Comments

In Channel

Lecture 15: Statistical Sins and Wrap Up

2017-05-1044:43

Lecture 14: Classification and Statistical Sins

2017-05-1049:25

Lecture 13: Classification

2017-05-1049:53

Lecture 12: Clustering

2017-05-1050:39

Lecture 11: Introduction to Machine Learning

2017-05-1051:30

Lecture 10: Understanding Experimental Data (cont

2017-05-1050:33

Lecture 9: Understanding Experimental Data

2017-05-1047:05

Lecture 8: Sampling and Standard Error

2017-05-1046:45

Lecture 7: Confidence Intervals

2017-05-1050:28

Lecture 6: Monte Carlo Simulation

2017-05-1050:04

Lecture 5: Random Walks

2017-05-1049:20

Lecture 4: Stochastic Thinking

2017-05-1049:49

Lecture 3: Graph-theoretic Models

2017-05-1050:11

Lecture 2: Optimization Problems

2017-05-1048:04

Lecture 1: Introduction and Optimization Problems

2017-05-1040:56

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Lecture 9: Understanding Experimental Data

#box-pro-ellipsis-176123960468236{-webkit-line-clamp:2;}Lecture 9: Understanding Experimental Data

Lecture 9: Understanding Experimental Data

Eric Grimson

Lecture 9: Understanding Experimental Data