Showing posts with label WELL-POSED LEARNING PROBLEMS. Show all posts
Showing posts with label WELL-POSED LEARNING PROBLEMS. Show all posts

Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs

UNIT-1 (A)
Introduction 

 Introduction:
  • Well-Posed Learning Problems
  • Designing a Learning System
  • Perspectives and Issues in Machine Learning
  • Introduction to Supervised
  • Unsupervised and Reinforcement Learning

 

Machine Learning 2- UNIT-1 (A) Notes : Introduction & Concept Learning and the General to Specific Ordering Notes

UNIT-1 
Introduction & Concept Learning and the General to Specific Ordering 

 Introduction:
  • Well-Posed Learning Problems
  • Designing a Learning System
  • Perspectives and Issues in Machine Learning
  • Introduction to Supervised
  • Unsupervised and Reinforcement Learning
 Concept Learning and the General to Specific Ordering:
  • Introduction
  • A Concept Learning Task
  • Concept Learning as Search
  • Find-S: Finding a Maximally Specific Hypothesis
  • Version Spaces and the Candidate Elimination Algorithm 

1. WELL-POSED LEARNING PROBLEMS

 

1.     WELL-POSED LEARNING PROBLEMS

Definition: A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

To have a well-defined learning problem, three features need to be identified:

1. The class of tasks

2. The measure of performance to be improved

3. The source of experience

 

Examples

1. Checkers game: A computer program that learns to play checkers might improve its performance as measured by its ability to win at the class of tasks involving playing checkers games, through experience obtained by playing games against itself.

Fig: Checker game board

A checkers learning problem:

        Task T: playing checkers

        Performance measure P: percent of games won against opponents

        Training experience E: playing practice games against itself

2. A handwriting recognition learning problem:

        Task T: recognizing and classifying handwritten words within images

        Performance measure P: percent of words correctly classified

        Training experience E: a database of handwritten words with given classifications

3. A robot driving learning problem:

        Task T: driving on public four-lane highways using vision sensors

        Performance measure P: average distance travelled before an error (as judged by human overseer)

        Training experience E: a sequence of images and steering commands recorded while observing a human driver

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