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introducción a Machine Learning

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CS 391L: Machine Learning Introduction Raymond J. Mooney University of Texas at Austin
What is Learning? ,[object Object],[object Object],[object Object],[object Object]
Classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem Solving / Planning / Control ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measuring Performance ,[object Object],[object Object],[object Object],[object Object]
Why Study Machine Learning? Engineering Better Computing Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Study Machine Learning? Cognitive Science ,[object Object],[object Object],[object Object],[object Object],[object Object],log(# training trials) log(perf. time)
Why Study Machine Learning? The Time is Ripe ,[object Object],[object Object],[object Object]
Related Disciplines ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining the Learning Task ,[object Object],[object Object],T: Playing checkers P: Percentage of games won against an arbitrary opponent E: Playing practice games against itself T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words T: Driving on four-lane highways using vision sensors P: Average distance traveled before a human-judged error E: A sequence of images and steering commands recorded while observing a human driver. T: Categorize email messages as spam or legitimate. P: Percentage of email messages correctly classified. E: Database of emails, some with human-given labels
Designing a Learning System ,[object Object],[object Object],[object Object],[object Object],Environment/ Experience Learner Knowledge Performance Element
Sample Learning Problem ,[object Object],[object Object]
Training Experience ,[object Object],[object Object],[object Object],[object Object],[object Object]
Source of Training Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Training vs. Test Distribution ,[object Object],[object Object],[object Object],[object Object]
Choosing a Target Function ,[object Object],[object Object],[object Object],[object Object],[object Object]
Ideal Definition of V ( b ) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Approximating V ( b ) ,[object Object],[object Object],[object Object],[object Object]
Representing the Target Function ,[object Object],[object Object],[object Object]
Linear Function for Representing V ( b ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Obtaining Training Values ,[object Object],[object Object],[object Object]
Temporal Difference Learning ,[object Object],[object Object],[object Object]
Learning Algorithm ,[object Object],[object Object],[object Object]
Least Mean Squares (LMS) Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
LMS Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object]
Lessons Learned about Learning ,[object Object],[object Object],[object Object]
Various Function Representations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Various Search Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation of Learning Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
History of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
History of Machine Learning (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
History of Machine Learning (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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introducción a Machine Learning

  • 1. CS 391L: Machine Learning Introduction Raymond J. Mooney University of Texas at Austin
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