Description
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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