This document discusses concept learning through analyzing examples to form hypotheses about concepts. It describes a set of possible days defined by attributes like sky, temperature, humidity, wind, and water conditions. There are 96 possible instances and over 5000 hypotheses that could describe the data. The working principle is explained using two examples to determine a "YES" response and find the most specific hypothesis that fits all examples.

1. Concept Learning
By Dr. Sambit Satpathy

4. ConceptLearning

5. Given instances X which represent a set of all possible days, each
described by the attributes:
o Sky – (values: Sunny, Cloudy, Rainy),
o Air Temp – (values: Warm, Cold),
o Humidity – (values: Normal, High),
o Wind – (values: Strong, Weak),
o Water – (values: Warm, Cold),
o Forecast – (values: Same, Change).

8. f
For Our Data
The Instance Space??? 3*2*2*2*2*2=96
Hypothesis Space?? 5*4*4*4*4*4=5120
Semantically Distinct Hypothesis?? 4*3*3*3*3*3+1= 973

9. Working Principle
Considered this 2 example
for “YES”

13. Maximal specific
hypothesis

14. Find Maximal specific hypothesis for given data set ?