IES 511 Machine Learning Dr. Türker İnce (Lecture notes by Prof. T. M IES 511 Machine Learning Dr. Türker İnce (Lecture notes by Prof. T. M. Mitchell, Machine Learning course at CMU) Concept Learning General-to-Specific Ordering of Hypothesis Find-S and Candidate Elimination Algorithms Inductive Bias

Learning System Design Example - Play Checkers

Concept Learning Example

Hypothesis representation

Concept Learning Task

Fundamental assumption of inductive learning

General-to-specific ordering of hypothesis

The Find-S Algorithm

Hypothesis space search by Find-S

Limitations of Find-S Can’t tell whether it has learned concept Can’t tell when training data inconsistent Picks a maximally specific h (why?) Depending on H, there might be several!

Consistent hypothesis and Version Space

The List-Then-Eliminate Algorithm

Version Space of EnjoySport Concept Learning

Version Space of EnjoySport Concept Learning

Candidate Elimination Algorithm

Candidate Elimination Algorithm

EnjoySport Example

EnjoySport Example

EnjoySport Example

Limitations of Candidate Elimination Training data contains errors Target concept is not in H, it can not be described in current hypothesis representation

Partially learned concept

Inductive Bias

Inductive learners modeled by equivalent deductive systems