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Machine Learning Fall, 2007 Course Information

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Machine Learning Fall, 2007
Course Information ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Grading Policy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Schedule ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction Machine Learning, Fall 2007
Well-Posed Learning Problems ,[object Object],[object Object]
Well-Posed Learning Problems : Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Well-Posed Learning Problems : Examples ,[object Object],[object Object],[object Object],[object Object]
Designing a Learning System ,[object Object],[object Object],[object Object],[object Object],[object Object]
Choosing the Training Experience ,[object Object],[object Object],[object Object],[object Object]
Choosing the Training Experience ,[object Object],[object Object],[object Object],[object Object]
Choosing the Training Experience ,[object Object],[object Object],[object Object]
Choosing the Target Function ,[object Object],[object Object],[object Object]
Choosing the Target Function ,[object Object]
Choosing a Representation for the Target Function ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Choosing a Function Approximation Algorithm
[object Object],[object Object],[object Object],Choosing a Function Approximation Algorithm For each training example <b, V train (b)> Use the current weights to calculate V’(b) For each weight w i , update it as w i  w i +  (V train (b) – V’(b)) x i
The Final Design ,[object Object],[object Object]
The Final Design ,[object Object]
The Final Design ,[object Object]
The Final Design Experiment Generator New problem (initial game board) Hypothesis Training examples Solution trace (game history) Figure 1.1 Final design of the checkers learning program Performance System Critic Generalizer { <b 1 ,V train (b 1 )> , <b 2 ,V train (b 2 )> …}
Choices in designing the checkers learning problem Linear programming Polynomial Linear function of six features Table of correct moves Games against self Games against experts Determine Type of Training Experience Determine Target Function Determine Representation of Learned Function Completed Design Determine Learning Algorithm … … … … Board  move Board  value Artificial neural network Gradient descent
Issues in Machine Learning ,[object Object],[object Object],[object Object]
Issues in Machine Learning ,[object Object],[object Object],[object Object]

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Machine Learning Fall, 2007 Course Information

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  • 22. The Final Design Experiment Generator New problem (initial game board) Hypothesis Training examples Solution trace (game history) Figure 1.1 Final design of the checkers learning program Performance System Critic Generalizer { <b 1 ,V train (b 1 )> , <b 2 ,V train (b 2 )> …}
  • 23. Choices in designing the checkers learning problem Linear programming Polynomial Linear function of six features Table of correct moves Games against self Games against experts Determine Type of Training Experience Determine Target Function Determine Representation of Learned Function Completed Design Determine Learning Algorithm … … … … Board  move Board  value Artificial neural network Gradient descent
  • 24.
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