Machine learing part 2

Bamberg University

Here are the best resources to pass Machine learing part 2. Find Machine learing part 2 study guides, notes, assignments, and much more.

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Instance Based Learning

Summary

Instance Based Learning
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January 2022

2004/2005
k-Nearest Neighbor Locally weighted regression Radial basis functions Case-based reasoning Lazy and eager learning
riyadhalgburi
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Evaluating Hypotheses

Summary

Evaluating Hypotheses
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January 2022

2004/2005
Sample error, true error Condence intervals for observed hypothesis error Estimators Binomial distribution, Normal distribution, Central Limit Theorem Paired t tests Comparing learning methods
riyadhalgburi
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Reinforcement Learning

Summary

Reinforcement Learning
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20

January 2022

2004/2005
Control learning Control policies that choose optimal actions Q learning Convergence
riyadhalgburi
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Combining Inductive and Analytical Learning

Summary

Combining Inductive and Analytical Learning
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17

January 2022

2004/2005
Why combine inductive and analytical learning? KBANN: Prior knowledge to initialize the hypothesis TangetProp, EBNN: Prior knowledge alters search ob jective FOCL: Prior knowledge alters search operators
riyadhalgburi
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The Inductive Generalization Problem

Summary

The Inductive Generalization Problem
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12

January 2022

2004/2005
• Two formulations for learning: Inductive and Analytical • Perfect domain theories and Prolog-EBG
riyadhalgburi
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Learning from examples

Summary

Learning from examples
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24

January 2022

2004/2005
Learning from examples General-to-specic ordering over hypotheses Version spaces and candidate elimination algorithm Picking new examples The need for inductive bias
riyadhalgburi
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Learning Sets of Rules

Summary

Learning Sets of Rules
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25

January 2022

2004/2005
Sequential covering algorithms FOIL Induction as inverse of deduction Inductive Logic Programming
riyadhalgburi
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Genetic Algorithms

Summary

Genetic Algorithms
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26

January 2022

2004/2005
Evolutionary computation Prototypical GA An example: GABIL Genetic Programming Individual learning and population evolution
riyadhalgburi
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Computational Learning Theory

Summary

Computational Learning Theory
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24

January 2022

2004/2005
Computational learning theory Setting 1: learner poses queries to teacher Setting 2: teacher chooses examples Setting 3: randomly generated instances, labeled by teacher Probably approximately correct (PAC) learning Vapnik-Chervonenkis Dimension Mistake bounds
riyadhalgburi
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Bayesian Learning

Summary

Bayesian Learning
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50

January 2022

2004/2005
Bayes Theorem MAP, ML hypotheses MAP learners Minimum description length principle Bayes optimal classier Naive Bayes learner Example: Learning over text data Bayesian belief networks Expectation Maximization algorithm
riyadhalgburi
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