1 / 21

Artificial Intelligence Course Introduction

Explore the historical development of artificial neural networks, knowledge representation, and machine learning. Understand the Perceptron, Widrow-Hoff Learning Rule, and future prospects in AI systems.

crhyne
Download Presentation

Artificial Intelligence Course Introduction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Artificial Neural NetworksECE.09.454/ECE.09.560Fall 2010 Lecture 1September 13, 2010 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall10/ann/

  2. http://www.youtube.com/watch?v=gy5g33S0Gzo March 17, 2010

  3. Plan • What is artificial intelligence? • Course introduction • Historical development – the neuron model • The artificial neural network paradigm • What is knowledge? What is learning? • The Perceptron • Widrow-Hoff Learning Rule • The “Future”….?

  4. Systems that think rationally • Logic • Systems that think like humans • Cognitive modeling • Systems that act rationally • Decision theoretic agents • Systems that act like humans • Natural language processing • Knowledge representation • Machine learning Artificial Intelligence

  5. Course Introduction • Why should we take this course? • PR, Applications • What are we studying in this course? • Course objectives/deliverables • How are we conducting this course? • Course logistics • http://engineering.rowan.edu/shreek/fall10/ann/

  6. Course Objectives • At the conclusion of this course the student will be able to: • Identify and describe engineering paradigms for knowledge and learning • Identify, describe and design artificial neural network architectures for simple cognitive tasks

  7. Biological Origins

  8. Biological Origins

  9. History/People

  10. Indicate Desired Outputs Determine Synaptic Weights Predicted Outputs Neural Network Paradigm Stage 1: Network Training Artificial Neural Network Present Examples “knowledge” Stage 2: Network Testing Artificial Neural Network New Data

  11. ANN Model x Input Vector y Output Vector Artificial Neural Network f Complex Nonlinear Function f(x) = y “knowledge”

  12. Single output ANN x y 1-out-of-c selector Coder Associator ANN ANN x x yc yc y2 y2 y1 y1 ANN x y Popular I/O Mappings

  13. The Perceptron Activation/ squashing function wk1 Bias, bk x1 wk2 x2 S S j(.) Output, yk Inputs uk Induced field, vk wkm xm Synaptic weights

  14. Activation Functions Threshold Sigmoid

  15. “Learning” Mathematical Model of the Learning Process Intitialize: Iteration (0) ANN [w]0 x y(0) [w] x y Iteration (1) [w]1 x y(1) desired o/p Iteration (n) [w]n x y(n) = d

  16. “Learning” Mathematical Model of the Learning Process Intitialize: Iteration (0) ANN [w]0 x y(0) [w] x y Iteration (1) [w]1 x y(1) desired o/p Iteration (n) [w]n x y(n) = d

  17. Error-Correction Learning Desired Output, dk (n) wk1(n) Activation/ squashing function x1 (n) Bias, bk wk2(n) x2 + Output, yk (n) S S j(.) Inputs Synaptic weights - Induced field, vk(n) wkm(n) Error Signal ek (n) xm

  18. Pattern Association Pattern Recognition Function Approximation Filtering x2 x2 2 2 DB 1 1 DB x1 x1 Learning Tasks Classification

  19. Perceptron Training Widrow-Hoff Rule (LMS Algorithm) w(0) = 0 n = 0 y(n) = sgn [wT(n) x(n)] w(n+1) = w(n) + h[d(n) – y(n)]x(n) n = n+1 Matlab Demo

  20. The Age of Spiritual MachinesWhen Computers Exceed Human Intelligenceby Ray Kurzweil | Penguin paperback | 0-14-028202-5 |

  21. Summary

More Related