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Introduction to Artificial Neural Networks: Course Overview and Learning Objectives

This lecture introduces the fundamentals of Artificial Neural Networks (ANN) and their historical development within the framework of Artificial Intelligence (AI). Key topics include the structure and function of neurons, the Perceptron model, the Widrow-Hoff Learning Rule, and various cognitive tasks. The course aims to equip students with the ability to design ANN architectures and understand knowledge and learning paradigms. The significance of systems that think rationally and like humans, along with applications in natural language processing and machine learning, will also be explored.

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Introduction to Artificial Neural Networks: Course Overview and Learning Objectives

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  1. Artificial Neural NetworksECE.09.454/ECE.09.560Fall 2008 Lecture 1September 8, 2008 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall08/ann/

  2. 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”….?

  3. 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

  4. 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/fall08/ann/

  5. 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

  6. Biological Origins

  7. Biological Origins

  8. History/People

  9. 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

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

  11. 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

  12. 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

  13. “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

  14. “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

  15. 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

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

  17. 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

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

  19. Summary

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