Signals and Systems Fall 2003 Lecture #1 Prof. Alan S ...

Signals and Systems Fall 2003 Lecture #1 Prof. Alan S ...

IES 511 Machine Learning Dr. Trker nce (Lecture notes by Prof. T. M. Mitchell, Machine Learning course at CMU)

1) 2) 3) 4) Concept Learning

General-to-Specific Ordering of Hypothesis Find-S and Candidate Elimination Algorithms Inductive Bias 1

Learning System Design Example - Play Checkers

2 Concept Learning Example 3

Hypothesis representation 4 Concept Learning Task

5 Fundamental assumption of inductive learning 6

General-to-specific ordering of hypothesis 7

The Find-S Algorithm 8 Hypothesis space search by Find-S

9 Limitations of Find-S 1) Cant tell whether it has learned concept 2) Cant tell when training data

inconsistent 3) Picks a maximally specific h (why?) 4) Depending on H, there might be several! 10

Consistent hypothesis and Version Space 11

The List-Then-Eliminate Algorithm 12 Version Space of EnjoySport Concept Learning

13 Version Space of EnjoySport Concept Learning

14 Candidate Elimination Algorithm 15

Candidate Elimination Algorithm 16 EnjoySport Example

17 EnjoySport Example 18

EnjoySport Example 19 Limitations of Candidate Elimination 1) Training data contains errors

2) Target concept is not in H, it can not be described in current hypothesis representation 20

Partially learned concept 21 Inductive Bias

22 Inductive learners modeled by equivalent deductive systems 23

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