PPT-Pattern Recognition and Machine Learning

Lucy Kuncheva School of Computer Science Bangor University mas00abangoracuk Part 1 1 What is Pattern Recognition Data set objects features class labels Classifiers and classifier ensembles. INTRODUCTION Pattern recognition stems from the need for automated machine recognition of objects signals or images or the need for automated decisionmaking based on a given set of parameters Despite over half a century of productive research patter . Course Introduction. Typical . Applications. Resources:. . Syllabus. Internet Books and Notes. D.H.S: Chapter 1. Glossary. LECTURE 01: . COURSE OVERVIEW. Pattern Recognition: . “the act of taking raw data and taking an action based on the category of the pattern.”. 2015 . GenCyber. Cybersecurity Workshop. An . Overview of . Biometrics. Dr. Charles C. Tappert. Seidenberg School of CSIS, Pace University. http://csis.pace.edu/~ctappert. /. . Biometrics Information Sources. . Pattern Recognition. John Beech. School of Psychology. PS1000.  . 2. Pattern Recognition. The term “pattern recognition” can refer to being able to . recognise. 2-D patterns, in particular alphanumerical characters. But “pattern recognition” is also understood to be the study of how we . Several slides from . Luke . Xettlemoyer. , . Carlos . Guestrin. and Ben . Taskar. Typical Paradigms of Recognition. Feature Computation. Model. Visual Recognition. Identification. Is this your car?. wearable accelerometers. Mitja Luštrek. Jožef Stefan Institute. Department of Intelligent Systems. Slovenia. Tutorial at the University of Bremen, November 2012. Outline. Accelerometers. Activity recognition with machine learning. Term Projects. CSE 666, . Fall 2014. Guidelines. The described projects are suggestions; if you have desire, skills or idea to explore alternative topics, you are free to do so.. . Finalize the project selection by October 16; have a 1-2 slide (2-3 minutes) presentation describing the project on that day.. 1. Speech Recognition and HMM Learning. Overview of speech recognition approaches. Standard Bayesian Model. Features. Acoustic Model Approaches. Language Model. Decoder. Issues. Hidden Markov Models. David Kauchak. CS 451 – Fall 2013. Why are you here?. What is Machine Learning?. Why are you taking this course?. What topics would you like to see covered?. Machine Learning is…. Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.. Clinical . Decision . Support of Pattern Perception. . that . “. makes it easy to do the right thing”. (IOM). Why . DISCIERNO. ?. C. urrent CDSS designs:. Lack a. dequate Preliminary . Symptom . Disorders. Richard J. Barohn, MD. Chair, Department of Neurology. Gertrude and Dewey Ziegler Professor of Neurology. University Distinguished Professor. Vice Chancellor for Research. University of Kansas Medical Center. Representation. Chumphol Bunkhumpornpat, Ph.D.. Department of Computer Science. Faculty of Science. Chiang Mai University. Learning Objectives. KDD Process. Know that patterns can be represented as. Vectors. (CS725). Autumn 2011. Instructor: . Prof. . Ganesh. . Ramakrishnan. TAs: . Ajay Nagesh, Amrita . Saha. , . Kedharnath. . Narahari. The grand goal. From the movie . 2001: A Space Odyssey. (1968). Outline. Pengtao. . Xie. 3/5/2014. 1. Outline. Gaussian Mixture Model. Expectation Maximization. 3/5/2014. 2. Motivation. 3/5/2014. 3. Formulation. 3/5/2014. 4. K clusters. GMM is a distribution. 3/5/2014. 5.