Lecture 02: Perceptron By: Nur Uddin, Ph.D.

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Presentation transcript:

Lecture 02: Perceptron By: Nur Uddin, Ph.D

History Artificial Intelligent - Lecture 2

History Pioneering work on neural network: McCulloch and Pitts (1943) for introducing the idea of neural networks as computing machines. Hebb (1949) for postulating the first rule for self-organized learning. Rosenblatt (1958) for proposing the perceptron as the first model for learning with a teacher (i.e., supervised learning). Artificial Intelligent - Lecture 2

Perceptron The perceptron is the simplest form of a neural network. It is used to classify linearly separable patterns. The learning algorithm was developed by Rosenblatt (1958, 1962) for his perceptron brain model. Artificial Intelligent - Lecture 2

Linearly Separable Patterns Artificial Intelligent - Lecture 2

Perceptron Model Rosenblatt’s perceptron is built around a nonlinear neuron, namely, the McCulloch–Pitts model of a neuron. Exercise: Calculate the output ! Artificial Intelligent - Lecture 2

Mathematics Model of Perceptron Artificial Intelligent - Lecture 2

Classification Question 1: How to make classification? Question 2: What is the activation function? Artificial Intelligent - Lecture 2

Decision Boundary Artificial Intelligent - Lecture 2

Learning Algorithm Artificial Intelligent - Lecture 2

Example 1: Restaurants Survey Price Taste Buy ? 5 6 Yes 7 3 No 8 9 10 Artificial Intelligent - Lecture 2

Example 1: Restaurants Survey (Cont’d) Price Taste Buy ? 5 6 1 7 3 8 9 10 Artificial Intelligent - Lecture 2

Classification using Perceptron Training Test (Generalization) Artificial Intelligent - Lecture 2

Example 1: Grading System Mid Final Score Grade 60 50 54 Fail 70 64 Pass 40 80 65 63 62 58 55 59 30 56 90 Score = Mid exam: 40% Final exam: 60% Grading system = Pass: score ≥ 60 Fail : score < 60 Artificial Intelligent - Lecture 2

Example 1: Grading System Mid Final Score Grade 60 50 54 Fail 70 64 Pass 40 80 65 63 62 58 55 59 30 56 90 Score = Mid exam: 40% Final exam: 60% Grading system = Pass: score ≥ 60 Fail : score < 60 Artificial Intelligent - Lecture 2

Exercise 1: Airlines Passenger Survey (Economy Class) Artificial Intelligent - Lecture 2