Motivation Computers are good at some things… Calculating 

Slides:

Advertisements

Similar presentations

Machine Learning: Connectionist McCulloch-Pitts Neuron Perceptrons Multilayer Networks Support Vector Machines Feedback Networks Hopfield Networks.

Advertisements

1 Neural Networks - Basics Artificial Neural Networks - Basics Uwe Lämmel Business School Institute of Business Informatics

Brian Merrick CS498 Seminar.  Introduction to Neural Networks  Types of Neural Networks  Neural Networks with Pattern Recognition  Applications.

Artificial Intelligence (CS 461D)

1 Part I Artificial Neural Networks Sofia Nikitaki.

1 Artificial Neural Networks: An Introduction S. Bapi Raju Dept. of Computer and Information Sciences, University of Hyderabad.

Neural Networks Dr. Peter Phillips. Neural Networks What are Neural Networks Where can neural networks be used Examples Recognition systems (Voice, Signature,

Introduction to Neural Network Justin Jansen December 9 th 2002.

Neural Networks Reading: Chapter 3 in Nilsson text Lab: Work on assignment 2 (due 19th in class)

Artificial intelligence 4 Expert systems 4 Neural nets 4 Data base mining.

1 MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING By Kaan Tariman M.S. in Computer Science CSCI 8810 Course Project.

Neural Networks Lab 5. What Is Neural Networks? Neural networks are composed of simple elements( Neurons) operating in parallel. Neural networks are composed.

Rohit Ray ESE 251. What are Artificial Neural Networks? ANN are inspired by models of the biological nervous systems such as the brain Novel structure.

Chapter 7 Artificial Neural Networks

Traffic Sign Recognition Using Artificial Neural Network Radi Bekker

Artificial Neural Networks:

Tying up loose ends.  Understand your data  No answers available, only data.

1 Neural Networks- a brief intro Dr Theodoros Manavis

MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way

1 st Neural Network: AND function Threshold(Y) = 2 X1 Y X Y.

Using Neural Networks in Database Mining Tino Jimenez CS157B MW 9-10:15 February 19, 2009.

Data Mining and Neural Networks Danny Leung CS157B, Spring 2006 Professor Sin-Min Lee.

Artificial Neural Nets and AI Connectionism Sub symbolic reasoning.

5.5 Learning algorithms. Neural Network inherits their flexibility and computational power from their natural ability to adjust the changing environments.

 The most intelligent device - “Human Brain”.  The machine that revolutionized the whole world – “computer”.  Inefficiencies of the computer has lead.

Outline What Neural Networks are and why they are desirable Historical background Applications Strengths neural networks and advantages Status N.N and.

Machine Learning Dr. Shazzad Hosain Department of EECS North South Universtiy

Presented by Scott Lichtor An Introduction to Neural Networks.

1 Introduction to Neural Networks And Their Applications.

Neural Networks II By Jinhwa Kim. 2 Neural Computing is a problem solving methodology that attempts to mimic how human brain function Artificial Neural.

Neural Networks in Computer Science n CS/PY 231 Lab Presentation # 1 n January 14, 2005 n Mount Union College.

Introduction to Neural Networks and Example Applications in HCI Nick Gentile.

 Based on observed functioning of human brain.  (Artificial Neural Networks (ANN)  Our view of neural networks is very simplistic.  We view a neural.

Inverse Kinematics for Robotics using Neural Networks. Authors: Sreenivas Tejomurtula., Subhash Kak

Reinforcement Learning AI – Week 22 Sub-symbolic AI Two: An Introduction to Reinforcement Learning Lee McCluskey, room 3/10

Artificial Neural Systems. Intro Artificial neural systems try to process information in the same way as the human brain does. Traditional computer systems.

C - IT Acumens. COMIT Acumens. COM. To demonstrate the use of Neural Networks in the field of Character and Pattern Recognition by simulating a neural.

Data Mining By: Johan Johansson. Mining Techniques Association Rules Association Rules Decision Trees Decision Trees Clustering Clustering Nearest Neighbor.

Neural Networks. Background - Neural Networks can be : Biological - Biological models Artificial - Artificial models - Desire to produce artificial systems.

1 Neural networks 2. 2 Introduction: Neural networks The nervous system contains 10^12 interconnected neurons.

SUPPORT VECTOR MACHINES Presented by: Naman Fatehpuria Sumana Venkatesh.

Network Management Lecture 13. MACHINE LEARNING TECHNIQUES 2 Dr. Atiq Ahmed Université de Balouchistan.

Artificial Neural Networks By: Steve Kidos. Outline Artificial Neural Networks: An Introduction Frank Rosenblatt’s Perceptron Multi-layer Perceptron Dot.

Artificial Neural Networks This is lecture 15 of the module `Biologically Inspired Computing’ An introduction to Artificial Neural Networks.

INTRODUCTION TO NEURAL NETWORKS 2 A new sort of computer What are (everyday) computer systems good at... and not so good at? Good at..Not so good at..

Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 6: Artificial Neural Networks for Data Mining.

Machine Learning for Computer Security

Fall 2004 Perceptron CS478 - Machine Learning.

Learning in Neural Networks

Soft Computing Introduction.

Artificial Intelligence (CS 370D)

Other Classification Models: Neural Network

Artificial Neural Networks

Neural Networks Dr. Peter Phillips.

What is an ANN ? The inventor of the first neuro computer, Dr. Robert defines a neural network as,A human brain like system consisting of a large number.

AV Autonomous Vehicles.

Introduction to Neural Networks And Their Applications

What is Pattern Recognition?

CSE 473 Introduction to Artificial Intelligence Neural Networks

Face Recognition with Neural Networks

network of simple neuron-like computing elements

Dr. Unnikrishnan P.C. Professor, EEE

Introduction to Neural Networks And Their Applications - Basics

Artificial Intelligence Lecture No. 28

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING

Fundamentals of Neural Networks Dr. Satinder Bal Gupta

III. Introduction to Neural Networks And Their Applications - Basics

Introduction to Neural Network

David Kauchak CS158 – Spring 2019

Presentation transcript:

Motivation Computers are good at some things… Calculating  Sorting lists Manipulating data i.e. anything involving a well-defined algorithm (involving “calculation”)

Motivation Computers are bad at some things… Recognizing faces Noticing patterns Making decisions i.e. things that humans do all the time!

Examples Image/sound recognition Faces/voices Text Problems/anomalies

Examples Decision-making Loan application Path navigation Path optimization Classification Sorting Who wrote this text?

Examples Prediction Stock market Football Purchasing habits/desires

Basic Idea of Neural Networks Since humans can easily solve certain problems that computers have a hard time solving… Why not try to mimic the brain’s behavior with a computer program?

Basic Idea of Neural Networks Humans: Observe input and/or output Numerous times Learn often by trial-and-error Extend observed cases to new observations

Basic Idea of Neural Networks Computer model: Function to turn input into output Learn from observed data Brain = highly interconnected network of simple elements Learning = adjusting (strength of) connections

InputOutput: Examples Image/sound recognition Faces/voices Text Problems/anomalies

InputOutput: Examples Decision-making Loan application Path navigation Path optimization Classification Sorting Who wrote this text?

InputOutput: Examples Prediction Stock market Football Purchasing habits/desires

Learning Supervised Reinforcement Unsupervised/associative