Introduction to Classification & Clustering

Slides:



Advertisements
Similar presentations
Introduction to Machine Learning BITS C464/BITS F464
Advertisements

Albert Gatt Corpora and Statistical Methods Lecture 13.
 In its most basic form, learning is perceptual classification  Perceptual classification involves judging or believing that an observed object, x, falls.
ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.
Introduction to Machine Learning Anjeli Singh Computer Science and Software Engineering April 28 th 2008.
The Decision-Making Process IT Brainpower
CIS 678 Artificial Intelligence problems deduction, reasoning knowledge representation planning learning natural language processing motion and manipulation.
Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley.
Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010.
Introduction to machine learning
CS Machine Learning. What is Machine Learning? Adapt to / learn from data  To optimize a performance function Can be used to:  Extract knowledge.
Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley.
Artificial Intelligence 6. Machine Learning, Version Space Method
Lecture 2: Introduction to Machine Learning
Introduction to Machine Learning MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way Based in part on notes from Gavin Brown, University of Manchester.
Using Neural Networks in Database Mining Tino Jimenez CS157B MW 9-10:15 February 19, 2009.
Teuvo Kohonen Dr. Eng., Emeritus Professor of the Academy of Finland; Academician Since the 1960s, Professor Kohonen has introduced several new concepts.
Machine Learning An Introduction. What is Learning?  Herbert Simon: “Learning is any process by which a system improves performance from experience.”
Lecture 10: 8/6/1435 Machine Learning Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
CS 445/545 Machine Learning Winter, 2012 Course overview: –Instructor Melanie Mitchell –Textbook Machine Learning: An Algorithmic Approach by Stephen Marsland.
Machine Learning Tutorial Amit Gruber The Hebrew University of Jerusalem.
Pattern Recognition April 19, 2007 Suggested Reading: Horn Chapter 14.
Week 1 - An Introduction to Machine Learning & Soft Computing
Artificial Intelligence 8. Supervised and unsupervised learning Japan Advanced Institute of Science and Technology (JAIST) Yoshimasa Tsuruoka.
Some questions -What is metadata? -Data about data.
Introduction Welcome Machine Learning.
Machine Learning. Definition Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational.
Azure Machine Learning Introduction to Azure ML. Setting Expectations This presentation is for you if…  you hear the buzzword “Machine Learning” and.
Introduction to Classification & Clustering Villanova University Machine Learning Lab Module 4.
Network Management Lecture 13. MACHINE LEARNING TECHNIQUES 2 Dr. Atiq Ahmed Université de Balouchistan.
Machine Learning with Spark MLlib
Machine Learning Models
Machine Learning for Computer Security
Information Organization: Overview
Artificial Intelligence, P.II
Machine Learning overview Chapter 18, 21
Machine Learning overview Chapter 18, 21
Learning from Data. Learning from Data Learning sensors actuators environment agent ? As an agent interacts with the world, it should learn about its.
Machine Learning: Introduction
Intro to Machine Learning
Machine Learning Slides: Isabelle Guyon, Erik Sudderth, Mark Johnson, Derek Hoiem, Lana Lazebnik Photo: CMU Machine Learning Department protests G20.
Data Mining 101 with Scikit-Learn
Machine Learning I & II.
Introduction to Data Science Lecture 7 Machine Learning Overview
Pattern Recognition Sergios Theodoridis Konstantinos Koutroumbas
Machine Learning Charan Puvvala.
Softmax Classifier + Generalization
Azure Machine Learning 101
MACHINE LEARNING.
What is Pattern Recognition?
Term Definition Examples Data Science Statistics with large data sets
An Introduction to Supervised Learning
Text Categorization Document classification categorizes documents into one or more classes which is useful in Information Retrieval (IR). IR is the task.
Classification of Matter Task Card Classification of Matter Task Card
Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John.
Supervised vs. unsupervised Learning
Introduction to Data Science Lesson 1
3.1.1 Introduction to Machine Learning
Lecture 6: Introduction to Machine Learning
Machine Learning: Introduction
Computational Thinking
Evaluating Classifiers
Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John.
Unit III: Biological Evolution
Information Organization: Overview
CS+Social Good.
Lecture 21: Machine Learning Overview AP Computer Science Principles
Machine Learning overview Chapter 18, 21
Azure Machine Learning
Lecture 9: Machine Learning Overview AP Computer Science Principles
Presentation transcript:

Introduction to Classification & Clustering Villanova University Machine Learning Project

Introduction to Classification & Clustering Machine Learning Getting a computer to learn from data A type of Artificial Intelligence, where a computer does something "intelligent" Ability of a computer to improve what it does in a way that mimics how humans learn, like with repetition or experience Villanova University Machine Learning Project Introduction to Classification & Clustering

Examples of Machine Learning Face detection used by Facebook to help you automatically tag friends Spam filters that get better over time at identifying and trashing spam emails Fraud detection that notices suspicious patterns of credit card use and you get a call Optical character recognition that reads the numbers written on a check you deposit Villanova University Machine Learning Project Introduction to Classification & Clustering

Classification & Clustering Two ways to put things into categories Classification Categories already exist Put each thing into the category where it fits best Clustering Categories don’t yet exist Put the things into brand new categories based on similar characteristics or features of the things Villanova University Machine Learning Project Introduction to Classification & Clustering

Introduction to Classification & Clustering Some of the things being put into groups are already labeled with their "class." These class labels are used to guide or supervise the classification of unlabeled things into one of the classes. When the classes are known ahead of time this type of machine learning is called: Supervised Learning Villanova University Machine Learning Project Introduction to Classification & Clustering

Example: Classification Dogs Cats Classify the photos on the next slide into one of these two categories Villanova University Machine Learning Project Introduction to Classification & Clustering

Example: Classification Villanova University Machine Learning Project Introduction to Classification & Clustering

How Did Classification Work? How did you approach the task? What made it easy to do? What made it hard to do? How good are your final categories? Villanova University Machine Learning Project Introduction to Classification & Clustering

Unsupervised Learning Introduction to Classification & Clustering None of the things being put into groups already have their class labels so the grouping is unsupervised. The task becomes figuring out clusters of things with similar features. When the classes are unknown ahead of time this type of machine learning is called: Unsupervised Learning Villanova University Machine Learning Project Introduction to Classification & Clustering

Introduction to Classification & Clustering Example: Clustering Group 1 Group 2 Cluster the photos on the next slide into two categories. You decide! Villanova University Machine Learning Project Introduction to Classification & Clustering

Introduction to Classification & Clustering Example: Clustering Villanova University Machine Learning Project Introduction to Classification & Clustering

How Did Clustering Work? How did you approach the task? What made it easy to do? What made it hard to do? How does it compare with Classification? How good are your final categories? Villanova University Machine Learning Project Introduction to Classification & Clustering

Example: Clustering Again! Group 1 Group 2 Group 3 Cluster the photos on the next slide into three categories. You decide! Villanova University Machine Learning Project Introduction to Classification & Clustering

Introduction to Classification & Clustering Example: Clustering Villanova University Machine Learning Project Introduction to Classification & Clustering

How Did Clustering Work? How did you create categories? Did it get harder to do with more categories? How good are your results? Villanova University Machine Learning Project Introduction to Classification & Clustering