Lecture26 - CS440/ECE448 Intro to Articial Intelligence...

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 26: A bit about learning theory Prof. Julia Hockenmaier [email protected] http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 2
Monday, May 2 , 4 pm in 1404 Siebel Center Natural Language Applications Across Genres: From News to Novels Prof. Kathleen McKeown, Columbia University Monday, May 2 , 6 pm in 2405 Siebel Center Attending Graduate School: A panel discussion Tuesday, May 3 , 10 am 2405 Siebel Center Machine Learning - Modern Times Dr Corinna Cortes (Head of Google Research, NY)
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Binary classifcation: training Input: {( x i, y i )} with (x 1…. x d ) ˥ R d y i ˥ {+1, -1} Task: Find weights w = (w 0 w 1…. w d ) ˥ R d+1 that defne f( x ) = wx 4 CS440/ECE448: Intro AI x 1 x 2 + + + + + + + + + x x x x x x x x x x Decision boundary f( x ) = 0
Background image of page 4
Boolean XOR XOR is not linearly separable 5 CS440/ECE448: Intro AI 0 1 x 1 x 2 1 0
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
From perceptrons to neural networks We can think of a single perceptron as one neuron 6 CS440/ECE448: Intro AI Output Σ Input Links Activation Function Input Function Output Links a 0 = 1 a j = g ( in j ) a j g in j w i,j w 0 ,j Bias Weight a i
Background image of page 6
Artifcial Neural Networks: Multi-layer perceptrons ! " # ! " # ! " # ! " # 7
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
From perceptrons to neural nets A neural net consists of nodes connected by directed links. Each node has an activation a i Links a ij propagate the activation a i from i to j. Each link has a weight w ij that determines the strength and sign of the connection 8 CS440/ECE448: Intro AI w 3,5 3,6 w 4,5 w 4,6 w 5 6 w 1,3 1,4 w 2,3 w 2,4 w 1 2 3 4 w 1,3 1,4 w 2,3 w 2,4 w 1 2 3 4 (b) (a)
Background image of page 8
From perceptrons to neural networks Each unit computes a weighted sum of its inputs: in j = ! j
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 10/13/2011 for the course CS 440 taught by Professor Levinson,s during the Spring '08 term at University of Illinois, Urbana Champaign.

Page1 / 28

Lecture26 - CS440/ECE448 Intro to Articial Intelligence...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online