Introduction to MachineLearning with Find-S

By: Girish Ch. Bharti

Sr. Software Consultant Knoldus Software LLP

Agenda

● A brief about machine learning

● What is inductive learning?

● What is concept learning

● Inductive bias

● Conjunctive concepts

● Find-S Algo

● Demo

Machine learning

Machine learning

Machine learning, AI & Deep learning

Applications of Machine learning

Machine learning (2017)

Machine learning workflow

Learning

● What is learning?

● Types of learning

● Passive learning (From teachers)Rote Learning (Memorization)Inductive learning (Learning through examples)

What is inductive learning?

● Learning by examples

● Ex:

-> Rote Learning:

2,4,6,8,10,12,14,16,18,20

-> Inductive learning:

2 * 1 = 2

2 * 2 = 4

2 * 3 = 6

2 * 4 = ?

First machine learning algo(Checkers , Early 60s)

What is machine learning?

Examples ->

Experience ->

Prediction

Machine learning problems● Identify apple and orange

Machine learning

● Features:

➢ Shape

➢ Color

➢ Taste

➢ Hardness

----------------------------

Weight?

Notion of data

● Data

● Learning

● Predictions

What is concept learning?

● “Problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples.”

Concept Learning

Concept Data Object

C X Label

x -Belogs to a concept c 1, +1, true

-Not belogs to a concept 0, -1, false

Note: Concept is not tied to data

Concept Learning

● Data objectx = ( circular, large, smooth, dark)

● Concept SpaceX = All possible values of feature valuesSize: => (No of features) power (possible values)=> 4 * 4 = 16

● Features

● Shape –> oval, circular

● Size -> large, small

● Color -> dark, light

● Surface -> smooth, irregular

Concept space

Conjuctive Concept

● X -> (cir, sm, dark, irr)

● X1 -> (1, 0, 1, 0)

● X2 -> (0, 0, 1, 1)

----------------------- h -> (0, 0, 1, 0)

● Circular: 1, Oval: 0

● Large: 1, Small: 0

● Dark: 1, Light: 0

● Smooth: 1, Irregular: 0

Conjuctive concept

● 1. Circular ^ Smooth

● 2. Large ^ Light ^ Irregular

● 3. Circular ^ Large ^ Dark ^ Smooth (Will lable only one as positive)

● 4. Oval (Will label 8 as positive)

Notations

● Circular ^ ? ^ ? ^ Smooth ---- (People in middle)

● ? ^ ? ^ ? ^ ? ---- (Accepts all)

● ⵁ (Fi) ---- (Rejects All)

● Total number of conjuctive concepts:

(3 * 3 * 3 * 3) + 1 = 82

If we have d binary features: ((3 power d) + 1 ) => Conjuctive concepts

Finding malignant tumor from MRI scan

Finding malignant tumor from MRI scan

● Attributes:

➢ Shape -> Circular, Oval

➢ Size -> Large, Small

➢ Color -> Light, Dark

➢ Surface -> Smooth, Irregular

● Concept: Malignent Tumor

Find S Algorighm

1) Start with h = ⵁ

2) Use next input {x, c(x)}

3) If c(x) = 0, go to step 2

4) h <- h ⴷ x (Pairwise AND)

5) If more examples : Go to step 2

6) Stop

Disadvantages of Find S

● Not accurate

● Do not consider negative examples

● Error prone

● Does not cove desjunctive concepts

Demo

DEMO

References

● https://blog.knoldus.com/2017/08/16/concept-learning-the-stepping-stone-towards-machine-learning-with-find-s/

● https://blog.knoldus.com/2017/11/01/concept-learning-find-s-implementation-with-scala/

● Wikipedia

● Tom Michel

Thanks