What is Pattern Recognition?

Slides:

Advertisements

Similar presentations

Introduction to Machine Learning BITS C464/BITS F464

Advertisements

Godfather to the Singularity

INTRODUCTION TO MACHINE LEARNING David Kauchak CS 451 – Fall 2013.

Lesson 6: Telemedicine. What is telemedicine? Telemedicine is the remote diagnosis and treatment of patients by means of telecommunications technology.

ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.

Introduction to Machine Learning Anjeli Singh Computer Science and Software Engineering April 28 th 2008.

CIS 678 Artificial Intelligence problems deduction, reasoning knowledge representation planning learning natural language processing motion and manipulation.

An Introduction to Machine Learning In the area of AI (earlier) machine learning took a back seat to Expert Systems Expert system development usually consists.

Intelligent Systems Group Emmanuel Fernandez Larry Mazlack Ali Minai (coordinator) Carla Purdy William Wee.

Lecture #1COMP 527 Pattern Recognition1 Pattern Recognition Why? To provide machines with perception & cognition capabilities so that they could interact.

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

Presented To: Madam Nadia Gul Presented By: Bi Bi Mariam.

Introduction to machine learning

Introduction to Data Mining Engineering Group in ACL.

CS Machine Learning. What is Machine Learning? Adapt to / learn from data  To optimize a performance function Can be used to:  Extract knowledge.

Introduction Lecture 1 Intro to ALS  These lecture notes accompany the book on ALS  They can be used with the book and the software for courses on.

Copyright R. Weber INFO 629 Concepts in Artificial Intelligence Fall 2004 Professor: Dr. Rosina Weber.

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications Lecture Notes by Neşe Yalabık Spring 2011.

Introduction Mohammad Beigi Department of Biomedical Engineering Isfahan University

MACHINE LEARNING 張銘軒譚恆力 1. OUTLINE OVERVIEW HOW DOSE THE MACHINE “ LEARN ” ? ADVANTAGE OF MACHINE LEARNING ALGORITHM TYPES  SUPERVISED.

Human Gesture Recognition Using Kinect Camera Presented by Carolina Vettorazzo and Diego Santo Orasa Patsadu, Chakarida Nukoolkit and Bunthit Watanapa.

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

Compiled By: Raj G Tiwari.  A pattern is an object, process or event that can be given a name.  A pattern class (or category) is a set of patterns sharing.

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.

Machine Learning Tutorial Amit Gruber The Hebrew University of Jerusalem.

1 Machine Learning 1.Where does machine learning fit in computer science? 2.What is machine learning? 3.Where can machine learning be applied? 4.Should.

1 Pattern Recognition Pattern recognition is: 1. A research area in which patterns in data are found, recognized, discovered, …whatever. 2. A catchall.

Course Instructor: K ashif I hsan 1. Chapter # 1 Kashif Ihsan, Lecturer CS, MIHE2.

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

Machine Learning Extract from various presentations: University of Nebraska, Scott, Freund, Domingo, Hong,

Machine Learning Introduction. Class Info Office Hours –Monday:11:30 – 1:00 –Wednesday:10:00 – 1:00 –Thursday:11:30 – 1:00 Course Text –Tom Mitchell:

يادگيري ماشين Machine Learning Lecturer: A. Rabiee

NATURAL LANGUAGE PROCESSING Zachary McNellis. Overview  Background  Areas of NLP  How it works?  Future of NLP  References.

Introduction to Machine Learning © Roni Rosenfeld,

Data Mining Concepts and Techniques Course Presentation by Ali A. Ali Department of Information Technology Institute of Graduate Studies and Research Alexandria.

Pattern Recognition NTUEE 高奕豪 2005/4/14. Outline Introduction Definition, Examples, Related Fields, System, and Design Approaches Bayesian, Hidden Markov.

Pattern Recognition. What is Pattern Recognition? Pattern recognition is a sub-topic of machine learning. PR is the science that concerns the description.

Introduction to Machine Learning August, 2014 Vũ Việt Vũ Computer Engineering Division, Electronics Faculty Thai Nguyen University of Technology.

Introduction to Classification & Clustering Villanova University Machine Learning Lab Module 4.

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.

CS570: Data Mining Spring 2010, TT 1 – 2:15pm Li Xiong.

Brief Intro to Machine Learning CS539

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Machine Learning with Spark MLlib

Artificial Intelligence

Introduction to Machine Learning

Electrical Engineering

Machine Learning for Computer Security

Introduction to Classification & Clustering

Artificial Intelligence, P.II

Eick: Introduction Machine Learning

School of Computer Science & Engineering

INFORMATION COMPRESSION, MULTIPLE ALIGNMENT, AND INTELLIGENCE

CH. 1: Introduction 1.1 What is Machine Learning Example:

Machine Learning Ali Ghodsi Department of Statistics

Mining Time-Series Databases

Basic Intro Tutorial on Machine Learning and Data Mining

Implementing AI solutions using the cognitive services in Azure

Joelle Pineau: General info

Data Warehousing and Data Mining

3.1.1 Introduction to Machine Learning

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Christoph F. Eick: A Gentle Introduction to Machine Learning

Machine learning CS 229 / stats 229

Presentation transcript:

What is Pattern Recognition?

What is Pattern Recognition? Pattern recognition is the act of taking in raw data and taking an action based on the category of the data Largely divided into supervised learning and unsupervised learning. It aims to classify data based on a priori knowledge or on statistical information extracted from the patterns. The pattern classified are groups of measurements or observations, defining points in a multidimensional space.

Where PR is Used? In medical science, computer-aided diagnosis Speech recognition Text classification (spam vs. non-spam) Human face recognition Image analysis Data mining Predictive analysis (stock pricing) Machine intelligence You name it

What is Machine Learning? Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data from sensors or databases. Major focus of ML is to automatically learn to recognize complex patterns and make intelligent decisions based on data. ML is closely related to statistics, probability theory, data mining, pattern recognition, artificial intelligence, adaptive control, and computer science.

Where ML is Used? Machine perception Computer vision Language processing Pattern recognition Search engines Medical diagnosis Bioinformatics Brain-machine interfaeces Stock market, DNA analysis, again you name it!