'Perceptron learning' diaporamas de présentation

Lecture 7

Lecture 7 . Artificial neural networks: Supervised learning. Introduction, or how the brain works The neuron as a simple computing element The perceptron Multilayer neural networks Accelerated learning in multilayer neural networks The Hopfield network

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Linear Functions

CS546: Machine Learning and Natural Language Lecture 7: Introduction to Classification: Linear Learning Algorithms 2009. {. 1 if w 1 x 1 + w 2 x 2 +. . . w n x n >= ? 0 Otherwise. f (x) =. y = x 1 ?? x 3 ?? x 5. Disjunctions:.

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Supervised learning

Supervised learning. Early learning algorithms First order gradient methods Second order gradient methods. Early learning algorithms. Designed for single layer neural networks Generally more limited in their applicability Some of them are Perceptron learning LMS or Widrow- Hoff learning

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Artificial Neural Networks

Articifial Intelligence. Artificial Neural Networks. Brian Talecki CSC 8520 Villanova University. ANN - Artificial Neural Network. A set of algebraic equations and functions which determine the best output given a set of inputs.

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For Monday

For Monday. Read Chapter 22, sections 4-6 No written homework. Program 4. Remember postponed until Wednesday week Any questions?. Picking the Best Literal. Based on information gain (similar to ID3). |p|*(log 2 (|p| /(|p|+|n|)) - log 2 (|P| /(|P|+|N|)))

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For Monday

For Monday. Finish chapter 22 FOIL mini-project due. Program 5. Learning mini-project. Worth 2 homeworks Due next Monday Foil6 is available in /home/mecalif/public/itk340/foil A manual and sample data files are there as well.

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????????????? ????? ????. 2010. Types of Machine Learnin g. 1. Unsupervised Learning: Only network inputs are available to the learning algorithm. The network is given only unlabeled examples. Network learns to categorize (cluster) the inputs. Example : Hebbian plasticity rule

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For Monday

For Monday. Read chapter 22, sections 1-3 No homework. Exam 2. Take-home due Friday Clarification questions? Review questions?. Program 4. Any questions?. Real Neurons. Cells forming the basis of neural tissue Cell body Dendrites Axon Syntaptic terminals

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Robotica

Lezione 10. Robotica. Lecture Outline. Neural networks Classical conditioning AHC with NNs Genetic Algorithms Classifier Systems Fuzzy learning Case-based learning Memory-based learning Explanation-based learning. Q Learning Algorithm.

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Computational Intelligence Winter Term 2011/12

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Computational Intelligence Winter Term 2009/10

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For Friday

For Friday. No reading Take home exam due Exam 2. For Monday. Read chapter 22, sections 1-3 FOIL exercise due. Model Neuron (Linear Threshold Unit). Neuron modelled by a unit ( j ) connected by weights, w ji , to other units ( i ): Net input to a unit is defined as:

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METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications. Part 7 : Linear and G eneralized D iscriminant F unctions. LINEAR DISCRIMINANT FUNCTIONS. Assume that t he discriminant functions are linear function s of X.

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Artificial Intelligence in Game Design

Artificial Intelligence in Game Design. Off-Line and Neural Network Learning in Games. Type of Learning in Games. On-Line learning Takes place during game Changes game parameters for this particular play of game Must be fast and efficient

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Artificial Intelligence in Game Design

Artificial Intelligence in Game Design. Lecture 24: Off-Line and Neural Network Learning in Games. Type of Learning in Games. On-Line learning Takes place during game Changes game parameters for this particular play of game Must be fast and efficient Simple hill climbing, N-Grams

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Chapter 2 Single Layer Feedforward Networks

Chapter 2 Single Layer Feedforward Networks. Perceptrons. By Rosenblatt (1962) For modeling visual perception (retina) A feedforward network of three layers of units: S ensory, A ssociation, and R esponse

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Announcements

Announcements. 1. Textbook will be on reserve at library 2. Topic schedule change; modified reading assignment: This week: Linear discrimination, evaluating classifiers Extra reading: T. Fawcett, An introduction to ROC analysis, Sections 1-4 (linked from class web page )

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Lecture 8. Learning (V): Perceptron Learning

Lecture 8. Learning (V): Perceptron Learning. OUTLINE. Perceptron Model Perceptron learning algorithm Convergence of Perceptron learning algorithm Example. PERCEPTRON. Consists of a single neuron with threshold activation, and binary output values.

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