CS 536 – Ahmed Elgammal - - 1 CS 536: Machine Learning Fall 2005 Ahmed Elgammal Dept of Computer Science Rutgers University.

Slides:



Advertisements
Similar presentations
Approaches, Tools, and Applications Islam A. El-Shaarawy Shoubra Faculty of Eng.
Advertisements

Introduction to Machine Learning BITS C464/BITS F464
Artificial Intelligence
Godfather to the Singularity
Applications of Embedded systems, Smart systems, A.I.
INTRODUCTION TO MACHINE LEARNING David Kauchak CS 451 – Fall 2013.
Artificial Intelligence. Intelligent? What is intelligence? computational part of the ability to achieve goals in the world.
C SC 421: Artificial Intelligence …or Computational Intelligence Alex Thomo
ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.
Machine Learning CSE 473. © Daniel S. Weld Topics Agency Problem Spaces Search Knowledge Representation Reinforcement Learning InferencePlanning.
Anomaly Detection brief review of my prospectus Ziba Rostamian CS590 – Winter 2008.
Learning Programs Danielle and Joseph Bennett (and Lorelei) 4 December 2007.
INTRODUCTION TO Machine Learning ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.
INTRODUCTION TO Machine Learning 3rd Edition
3.11 Robotics, artificial intelligence and expert systems Strand 3 Karley Holland.
CS Machine Learning. What is Machine Learning? Adapt to / learn from data  To optimize a performance function Can be used to:  Extract knowledge.
Copyright R. Weber INFO 629 Concepts in Artificial Intelligence Fall 2004 Professor: Dr. Rosina Weber.
Notes for CS3310 Artificial Intelligence Part 1: Overview Prof. Neil C. Rowe Naval Postgraduate School Version of January 2009.
Xiaoying Sharon Gao Mengjie Zhang Computer Science Victoria University of Wellington Introduction to Artificial Intelligence COMP 307.
Artificial Intelligence: Its Roots and Scope
Introduction to Machine Learning MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way Based in part on notes from Gavin Brown, University of Manchester.
MACHINE LEARNING 張銘軒 譚恆力 1. OUTLINE OVERVIEW HOW DOSE THE MACHINE “ LEARN ” ? ADVANTAGE OF MACHINE LEARNING ALGORITHM TYPES  SUPERVISED.
Artificial Intelligence Introductory Lecture Jennifer J. Burg Department of Mathematics and Computer Science.
10/6/2015 1Intelligent Systems and Soft Computing Lecture 0 What is Soft Computing.
Machine Learning An Introduction. What is Learning?  Herbert Simon: “Learning is any process by which a system improves performance from experience.”
Machine Learning Lecture 1. Course Information Text book “Introduction to Machine Learning” by Ethem Alpaydin, MIT Press. Reference book “Data Mining.
Lecture 10: 8/6/1435 Machine Learning Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
G52IVG, School of Computer Science, University of Nottingham 1 Administrivia Timetable Lectures, Friday 14:00 – 16:00 Labs, Friday 17:00 -18:00 Assessment.
Yazd University, Electrical and Computer Engineering Department Course Title: Advanced Software Engineering By: Mohammad Ali Zare Chahooki 1 Introduction.
Overview of Part I, CMSC5707 Advanced Topics in Artificial Intelligence KH Wong (6 weeks) Audio signal processing – Signals in time & frequency domains.
1 CS 2710, ISSP 2610 Foundations of Artificial Intelligence introduction.
What is Real Now? Artificial Intelligence By Geena Yarbrough.
Artificial Intelligence: Introduction Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
Dr. Hammad Majeed Areas of Interest o Artificial Intelligence and Machine Learning Evolutionary Algorithms Data Mining Image Processing.
More Computer Science in your Future? CSE 142 Autumn
Spring 2016 Graduate Preview November 3, Spring 2016 Graduate Courses CS 532 – Web Science R 4:20-7:00pm Nelson CS 550 – Database Concepts ONLINE.
ARTIFICIALINTELLIGENCE ARTIFICIAL INTELLIGENCE EXPERT SYSTEMS.
Introduction to Artificial Intelligence CS 438 Spring 2008.
يادگيري ماشين Machine Learning Lecturer: A. Rabiee
WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.
Miloš Kotlar 2012/115 Single Layer Perceptron Linear Classifier.
Intelligent Control Methods Lecture 2: Artificial Intelligence Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Artificial Intelligence, simulation and modelling.
Pattern Recognition NTUEE 高奕豪 2005/4/14. Outline Introduction Definition, Examples, Related Fields, System, and Design Approaches Bayesian, Hidden Markov.
  Computer vision is a field that includes methods for acquiring,prcessing, analyzing, and understanding images and, in general, high-dimensional data.
1 Artificial Intelligence & Prolog Programming CSL 302.
Introduction to Machine Learning August, 2014 Vũ Việt Vũ Computer Engineering Division, Electronics Faculty Thai Nguyen University of Technology.
COMPUTER SYSTEM FUNDAMENTAL Genetic Computer School INTRODUCTION TO ARTIFICIAL INTELLIGENCE LESSON 11.
AZURE MACHINE LEARNING Bringing New Value To Old Data SQL Saturday #
By William Campbell MACHINE LEARNING. OVERVIEW What is machine learning? Human decision-making Learning algorithms Applications.
Network Management Lecture 13. MACHINE LEARNING TECHNIQUES 2 Dr. Atiq Ahmed Université de Balouchistan.
Introduction to Artificial Intelligence Heshaam Faili University of Tehran.
Course Outline (6 Weeks) for Professor K.H Wong
Machine Learning, Bio-informatics and Weka
Artificial Intelligence, P.II
Computational UIUC Lane Schwartz Student Orientation August 18, 2016.
Eick: Introduction Machine Learning
Intro to Machine Learning

INFORMATION COMPRESSION, MULTIPLE ALIGNMENT, AND INTELLIGENCE
Machine Learning With Python Sreejith.S Jaganadh.G.
Application Areas of Artificial Intelligence(AI)
Machine Learning Dr. Mohamed Farouk.
What is Pattern Recognition?
Course Instructor: knza ch
T H E P U B G P R O J E C T.
Christoph F. Eick: A Gentle Introduction to Machine Learning
Lecture 21: Machine Learning Overview AP Computer Science Principles
AI Application Session 12
Lecture 9: Machine Learning Overview AP Computer Science Principles
Presentation transcript:

CS 536 – Ahmed Elgammal CS 536: Machine Learning Fall 2005 Ahmed Elgammal Dept of Computer Science Rutgers University

CS 536 – Ahmed Elgammal Outlines Class policies What is machine learning Some basics

CS 536 – Ahmed Elgammal Machine Learning ?

CS 536 – Ahmed Elgammal What is machine learning (From Wikipedia) Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets. Machine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations. Many inference problems turn out to be NP-hard so part of machine learning research is the development of tractable approximate inference algorithms. Machine learning has a wide spectrum of applications including search engines, medical diagnosis, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, game playing and robot locomotion.

CS 536 – Ahmed Elgammal ,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa 7.0,3.2,4.7,1.4,Iris-versicolor 6.4,3.2,4.5,1.5,Iris-versicolor 6.9,3.1,4.9,1.5,Iris-versicolor 5.5,2.3,4.0,1.3,Iris-versicolor 6.4,2.7,5.3,1.9,Iris-virginica 6.8,3.0,5.5,2.1,Iris-virginica 5.7,2.5,5.0,2.0,Iris-virginica 5.8,2.8,5.1,2.4,Iris-virginica 6.4,3.2,5.3,2.3,Iris-virginica

CS 536 – Ahmed Elgammal Sources