Introduction to Machine Learning


 Hilary Chapman
 2 years ago
 Views:
Transcription
1 Introduction to Machine Learning Isabelle Guyon
2 What is Machine Learning? Learning algorithm Trained machine TRAINING DATA Answer Query
3 What for? Classification Time series prediction Regression Clustering
4 Some Learning Machines Linear models Kernel methods Neural networks Decision trees
5 Applications training examples Ecology System diagnosis Market Analysis OCR HWR Machine Vision Text Categorization Bioinformatics inputs
6 Banking / Telecom / Retail Identify: Prospective customers Dissatisfied customers Good customers Bad payers Obtain: More effective advertising Less credit risk Fewer fraud Decreased churn rate
7 Biomedical / Biometrics Medicine: Screening Diagnosis and prognosis Drug discovery Security: Face recognition Signature / fingerprint / iris verification DNA fingerprinting 6
8 Computer / Internet Computer interfaces: Troubleshooting wizards Handwriting and speech Brain waves Internet Hit ranking Spam filtering Text categorization Text translation Recommendation 7
9 Challenges training examples 10 5 Sylva Ada NIPS 2003 & WCCI Dexter, Nova Madelon Gisette Gina Arcene, Dorothea, Hiva inputs
10 Ten Classification Tasks ARCENE DEXTER DOROTHEA GISETTE MADELON Test BER (%) ADA GINA HIVA NOVA SYLVA
11 Challenge Winning Methods BER/<BER> Linear /Kernel Neural Nets Trees /RF Naïve Bayes Gisette (HWR) Gina (HWR) Dexter (Text) Nova (Text) Madelon (Artificial) Arcene (Spectral) Dorothea (Pharma) Hiva (Pharma) Ada (Marketing) Sylva (Ecology)
12 Conventions n X={x ij } y ={y x m j } i α w
13 Learning problem Data matrix: X m lines = patterns (data points, examples): samples, patients, documents, images, n columns = features: (attributes, input variables): genes, proteins, words, pixels, Colon cancer, Alon et al 1999 Unsupervised learning Is there structure in data? Supervised learning Predict an outcome y.
14 Linear Models f(x) = w x +b = Σ j=1:n w j x j +b Linearity in the parameters, NOT in the input components. f(x) = w Φ(x) +b = Σ j w j φ j (x) +b (Perceptron) f(x) = Σ i=1:m α i k(x i,x) +b (Kernel method)
15 Artificial Neurons x 1 Cell potential w 1 x 2 w 2 Σ f(x) Activation of other neurons x n 1 w n b Synapses Dendrites Axon Activation function McCulloch and Pitts, 1943 f(x) = w x + b
16 Linear Decision Boundary hyperplane 0.5 x x 3 X x x X xx
17 Perceptron x 1 φ 1 (x) Rosenblatt, 1957 x 2 φ 2 (x) w 1 w 2 Σ f(x) x n φ N (x) 1 w N b f(x) = w Φ(x) + b
18 NL Decision Boundary x Hs.7780 x x 1 x 2 Hs x 1 Hs
19 Kernel Method x 1 k(x 1,x) Potential functions, Aizerman et al 1964 x 2 k(x 2,x) α 1 α 2 Σ x n k(x m,x) 1 α m b f(x) = Σ i α i k(x i,x) + b k(.,. ) is a similarity measure or kernel.
20 Hebb s Rule w j w j + y i x ij Activation of another neuron x j w j Σ y Axon Dendrite Synapse Link to Naïve Bayes
21 Kernel Trick (for Hebb s rule) Hebb s rule for the Perceptron: w = Σ i y i Φ(x i ) f(x) = w Φ(x) = Σ i y i Φ(x i ) Φ(x) Define a dot product: k(x i,x) = Φ(x i ) Φ(x) f(x) = Σ i y i k(x i,x)
22 Kernel Trick (general) f(x) = Σ i α i k(x i, x) k(x i, x) = Φ(x i ) Φ(x) Dual forms f(x) = w Φ(x) w = Σ i α i Φ(x i )
23 What is a Kernel? A kernel is: a similarity measure a dot product in some feature space: k(s, t) = Φ(s) Φ(t) But we do not need to know the Φ representation. Examples: k(s, t) = exp( st 2 /σ 2 ) k(s, t) = (s t) q Gaussian kernel Polynomial kernel
24 MultiLayer Perceptron Backpropagation, Rumelhart et al, 1986 Σ x j Σ Σ internal latent variables hidden units
25 Chessboard Problem
26 Tree Classifiers CART (Breiman, 1984) or C4.5 (Quinlan, 1993) f 2 All the data Choose f 1 Choose f 2 f 1 At each step, choose the feature that reduces entropy most. Work towards node purity.
27 Linear discriminant Iris Data (Fisher, 1936) Figure from Norbert Jankowski and Krzysztof Grabczewski Tree classifier setosa virginica versicolor Gaussian mixture Kernel method (SVM)
28 Fit / Robustness Tradeoff x 2 x 2 x 1 x 1 15
29 Performance evaluation f(x) = 0 f(x) < 0 f(x) < 0 x 2 x 2 f(x) = 0 f(x) > 0 f(x) > 0 x 1 x 1
30 Performance evaluation f(x) = 1 f(x) < 1 f(x) < 1 x 2 x 2 f(x) = 1 f(x) > 1 f(x) > 1 x 1 x 1
31 Performance evaluation f(x) = 1 f(x) < 1 f(x) < 1 x 2 x 2 f(x) = 1 f(x) > 1 f(x) > 1 x 1 x 1
32 ROC Curve For a given threshold on f(x), you get a point on the ROC curve. 100% Ideal ROC curve Actual ROC Positive class success rate (hit rate, sensitivity) Random ROC negative class success rate (false alarm rate, 1specificity) 100%
33 ROC Curve For a given threshold on f(x), you get a point on the ROC curve. 100% Positive class success rate (hit rate, sensitivity) Ideal ROC curve (AUC=1) Actual ROC Random ROC (AUC=0.5) 0 0 AUC negative class success rate (false alarm rate, 1specificity) 100%
34 Lift Curve Customers ranked according to f(x); selection of the top ranking customers. Gini = M O Gini=2 AUC1 0 Gini 1 100% Hit rate = Frac. good customers select. 0 O Ideal Lift Actual Lift M Random lift Fraction of customers selected 100%
35 Performance Assessment Cost matrix Truth: y Predictions: F(x) Class 1 Class +1 Class 1 tn fp Class +1 fn tp Total Class+1 /Total rej=tn+fn sel=fp+tp Precision = tp/sel Total neg=tn+fp pos=fn+tp Class +1 / Total False alarm = fp/neg Hit rate = tp/pos m=tn+fp Frac. selected = sel/m +fn+tp False alarm rate = type I errate = 1specificity Hit rate = 1type II errate = sensitivity = recall = test power Compare F(x) = sign(f(x)) to the target y, and report: Error rate = (fn + fp)/m {Hit rate, False alarm rate} or {Hit rate, Precision} or {Hit rate, Frac.selected} Balanced error rate (BER) = (fn/pos + fp/neg)/2 = 1 (sensitivity+specificity)/2 F measure = 2 precision.recall/(precision+recall) Vary the decision threshold θ in F(x) = sign(f(x)+θ), and plot: ROC curve: Hit rate vs. False alarm rate Lift curve: Hit rate vs. Fraction selected Precision/recall curve: Hit rate vs. Precision
36 What is a Risk Functional? A function of the parameters of the learning machine, assessing how much it is expected to fail on a given task. Examples: Classification: Error rate: (1/m) Σ i=1:m 1(F(x i ) y i ) 1 AUC (Gini Index = 2 AUC1) Regression: Mean square error: (1/m) Σ i=1:m (f(x i )y i ) 2
37 How to train? Define a risk functional R[f(x,w)] Optimize it w.r.t. w (gradient descent, mathematical programming, simulated annealing, genetic algorithms, etc.) R[f(x,w)] Parameter space (w) w* ( to be continued in the next lecture)
38 How to Train? Define a risk functional R[f(x,w)] Find a method to optimize it, typically gradient descent w j w j  η R/ w j or any optimization method (mathematical programming, simulated annealing, genetic algorithms, etc.) ( to be continued in the next lecture)
39 Summary With linear threshold units ( neurons ) we can build: Linear discriminant (including Naïve Bayes) Kernel methods Neural networks Decision trees The architectural hyperparameters may include: The choice of basis functions φ (features) The kernel The number of units Learning means fitting: Parameters (weights) Hyperparameters Be aware of the fit vs. robustness tradeoff
40 Want to Learn More? Pattern Classification, R. Duda, P. Hart, and D. Stork. Standard pattern recognition textbook. Limited to classification problems. Matlab code. The Elements of statistical Learning: Data Mining, Inference, and Prediction. T. Hastie, R. Tibshirani, J. Friedman, Standard statistics textbook. Includes all the standard machine learning methods for classification, regression, clustering. R code. Linear Discriminants and Support Vector Machines, I. Guyon and D. Stork, In Smola et al Eds. Advances in Large Margin Classiers. Pages , MIT Press, Feature Extraction: Foundations and Applications. I. Guyon et al, Eds. Book for practitioners with datasets of NIPS 2003 challenge, tutorials, best performing methods, Matlab code, teaching material.
Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network?  Perceptron learners  Multilayer networks What is a Support
More informationNeural networks. Chapter 20, Section 5 1
Neural networks Chapter 20, Section 5 Chapter 20, Section 5 Outline Brains Neural networks Perceptrons Multilayer perceptrons Applications of neural networks Chapter 20, Section 5 2 Brains 0 neurons of
More informationIntroduction to Machine Learning NPFL 054
Introduction to Machine Learning NPFL 054 http://ufal.mff.cuni.cz/course/npfl054 Barbora Hladká hladka@ufal.mff.cuni.cz Martin Holub holub@ufal.mff.cuni.cz Charles University, Faculty of Mathematics and
More informationReview of some concepts in predictive modeling
Review of some concepts in predictive modeling Brigham and Women s Hospital HarvardMIT Division of Health Sciences and Technology HST.951J: Medical Decision Support A disjoint list of topics? Naïve Bayes
More informationCS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.
Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott
More informationIntroduction to Machine Learning
Introduction to Machine Learning Brown University CSCI 1950F, Spring 2012 Prof. Erik Sudderth Lecture 5: Decision Theory & ROC Curves Gaussian ML Estimation Many figures courtesy Kevin Murphy s textbook,
More informationC19 Machine Learning
C9 Machine Learning 8 Lectures Hilary Term 25 2 Tutorial Sheets A. Zisserman Overview: Supervised classification perceptron, support vector machine, loss functions, kernels, random forests, neural networks
More informationLearning. Artificial Intelligence. Learning. Types of Learning. Inductive Learning Method. Inductive Learning. Learning.
Learning Learning is essential for unknown environments, i.e., when designer lacks omniscience Artificial Intelligence Learning Chapter 8 Learning is useful as a system construction method, i.e., expose
More information203.4770: Introduction to Machine Learning Dr. Rita Osadchy
203.4770: Introduction to Machine Learning Dr. Rita Osadchy 1 Outline 1. About the Course 2. What is Machine Learning? 3. Types of problems and Situations 4. ML Example 2 About the course Course Homepage:
More informationIntroduction to Machine Learning Using Python. Vikram Kamath
Introduction to Machine Learning Using Python Vikram Kamath Contents: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Introduction/Definition Where and Why ML is used Types of Learning Supervised Learning Linear Regression
More informationClass #6: Nonlinear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris
Class #6: Nonlinear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Nonlinear classification Linear Support Vector Machines
More informationCS 2750 Machine Learning. Lecture 1. Machine Learning. CS 2750 Machine Learning.
Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott
More informationDrug Store Sales Prediction
Drug Store Sales Prediction Chenghao Wang, Yang Li Abstract  In this paper we tried to apply machine learning algorithm into a real world problem drug store sales forecasting. Given store information,
More informationClassifiers & Classification
Classifiers & Classification Forsyth & Ponce Computer Vision A Modern Approach chapter 22 Pattern Classification Duda, Hart and Stork School of Computer Science & Statistics Trinity College Dublin Dublin
More informationData Mining Practical Machine Learning Tools and Techniques
Counting the cost Data Mining Practical Machine Learning Tools and Techniques Slides for Section 5.7 In practice, different types of classification errors often incur different costs Examples: Loan decisions
More informationChapter 7. Diagnosis and Prognosis of Breast Cancer using Histopathological Data
Chapter 7 Diagnosis and Prognosis of Breast Cancer using Histopathological Data In the previous chapter, a method for classification of mammograms using wavelet analysis and adaptive neurofuzzy inference
More informationMachine Learning. 01  Introduction
Machine Learning 01  Introduction Machine learning course One lecture (Wednesday, 9:30, 346) and one exercise (Monday, 17:15, 203). Oral exam, 20 minutes, 5 credit points. Some basic mathematical knowledge
More informationAcknowledgments. Data Mining with Regression. Data Mining Context. Overview. Colleagues
Data Mining with Regression Teaching an old dog some new tricks Acknowledgments Colleagues Dean Foster in Statistics Lyle Ungar in Computer Science Bob Stine Department of Statistics The School of the
More informationCOMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection.
COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection. Instructor: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp551 Unless otherwise
More informationAssessing Data Mining: The State of the Practice
Assessing Data Mining: The State of the Practice 2003 Herbert A. Edelstein Two Crows Corporation 10500 Falls Road Potomac, Maryland 20854 www.twocrows.com (301) 9833555 Objectives Separate myth from reality
More informationEECS 445: Introduction to Machine Learning Winter 2015
Instructor: Prof. Jenna Wiens Office: 3609 BBB wiensj@umich.edu EECS 445: Introduction to Machine Learning Winter 2015 Graduate Student Instructor: Srayan Datta Office: 3349 North Quad (**office hours
More informationData Mining Algorithms Part 1. Dejan Sarka
Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses
More informationIntroduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011
Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning
More informationLecture 1: Introduction to Neural Networks Kevin Swingler / Bruce Graham
Lecture 1: Introduction to Neural Networks Kevin Swingler / Bruce Graham kms@cs.stir.ac.uk 1 What are Neural Networks? Neural Networks are networks of neurons, for example, as found in real (i.e. biological)
More informationFeature Subset Selection in Email Spam Detection
Feature Subset Selection in Email Spam Detection Amir Rajabi Behjat, Universiti Technology MARA, Malaysia IT Security for the Next Generation Asia Pacific & MEA Cup, Hong Kong 1416 March, 2012 Feature
More informationMachine Learning model evaluation. Luigi Cerulo Department of Science and Technology University of Sannio
Machine Learning model evaluation Luigi Cerulo Department of Science and Technology University of Sannio Accuracy To measure classification performance the most intuitive measure of accuracy divides the
More informationIntroduction to Neural Networks for Senior Design
Introduction to Neural Networks for Senior Design Intro1 Neural Networks: The Big Picture Artificial Intelligence Neural Networks Expert Systems Machine Learning not ruleoriented ruleoriented Intro2
More informationMachine Learning CS 6830. Lecture 01. Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu
Machine Learning CS 6830 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu What is Learning? MerriamWebster: learn = to acquire knowledge, understanding, or skill
More informationIntroduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk
Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trakovski trakovski@nyus.edu.mk Neural Networks 2 Neural Networks Analogy to biological neural systems, the most robust learning systems
More informationMA2823: Foundations of Machine Learning
MA2823: Foundations of Machine Learning École Centrale Paris Fall 2015 ChloéAgathe Azencot Centre for Computational Biology, Mines ParisTech chloe agathe.azencott@mines paristech.fr TAs: Jiaqian Yu jiaqian.yu@centralesupelec.fr
More informationIntroduction to Pattern Recognition
Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2009 CS 551, Spring 2009 c 2009, Selim Aksoy (Bilkent University)
More informationIntroduction to Artificial Neural Networks. Introduction to Artificial Neural Networks
Introduction to Artificial Neural Networks v.3 August Michel Verleysen Introduction  Introduction to Artificial Neural Networks p Why ANNs? p Biological inspiration p Some examples of problems p Historical
More informationLecture 6. Artificial Neural Networks
Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm
More informationBIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376
Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM 10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.
More informationData Mining  Evaluation of Classifiers
Data Mining  Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010
More informationBig Data Analytics CSCI 4030
High dim. data Graph data Infinite data Machine learning Apps Locality sensitive hashing PageRank, SimRank Filtering data streams SVM Recommen der systems Clustering Community Detection Web advertising
More informationPredictive Data modeling for health care: Comparative performance study of different prediction models
Predictive Data modeling for health care: Comparative performance study of different prediction models Shivanand Hiremath hiremat.nitie@gmail.com National Institute of Industrial Engineering (NITIE) Vihar
More informationPerformance Measures for Machine Learning
Performance Measures for Machine Learning 1 Performance Measures Accuracy Weighted (CostSensitive) Accuracy Lift Precision/Recall F Break Even Point ROC ROC Area 2 Accuracy Target: 0/1, 1/+1, True/False,
More informationFeedForward mapping networks KAIST 바이오및뇌공학과 정재승
FeedForward mapping networks KAIST 바이오및뇌공학과 정재승 How much energy do we need for brain functions? Information processing: Tradeoff between energy consumption and wiring cost Tradeoff between energy consumption
More informationArtificial neural networks
Artificial neural networks Now Neurons Neuron models Perceptron learning Multilayer perceptrons Backpropagation 2 It all starts with a neuron 3 Some facts about human brain ~ 86 billion neurons ~ 10 15
More informationDetection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup
Network Anomaly Detection A Machine Learning Perspective Dhruba Kumar Bhattacharyya Jugal Kumar KaKta»C) CRC Press J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor
More informationCLASSIFICATION JELENA JOVANOVIĆ. Web:
CLASSIFICATION JELENA JOVANOVIĆ Email: jeljov@gmail.com Web: http://jelenajovanovic.net OUTLINE What is classification? Binary and multiclass classification Classification algorithms Performance measures
More informationObtaining Value from Big Data
Obtaining Value from Big Data Course Notes in Transparency Format technology basics for data scientists Spring  2014 Jordi Torres, UPC  BSC www.jorditorres.eu @JordiTorresBCN Data deluge, is it enough?
More informationSearching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries
Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries 2015 SURE Presentation September 22 nd, 2015 Lau Ka Tung Department of Physics, The Chinese University of Hong Kong
More informationMachine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu
Machine Learning CUNY Graduate Center, Spring 2013 Professor Liang Huang huang@cs.qc.cuny.edu http://acl.cs.qc.edu/~lhuang/teaching/machinelearning Logistics Lectures M 9:3011:30 am Room 4419 Personnel
More informationINTRODUCTION TO MACHINE LEARNING 3RD EDITION
ETHEM ALPAYDIN The MIT Press, 2014 Lecture Slides for INTRODUCTION TO MACHINE LEARNING 3RD EDITION alpaydin@boun.edu.tr http://www.cmpe.boun.edu.tr/~ethem/i2ml3e CHAPTER 1: INTRODUCTION Big Data 3 Widespread
More informationLinear Classification. Volker Tresp Summer 2015
Linear Classification Volker Tresp Summer 2015 1 Classification Classification is the central task of pattern recognition Sensors supply information about an object: to which class do the object belong
More informationMachine Learning (CS 567)
Machine Learning (CS 567) Time: TTh 5:00pm  6:20pm Location: GFS 118 Instructor: Sofus A. Macskassy (macskass@usc.edu) Office: SAL 216 Office hours: by appointment Teaching assistant: Cheol Han (cheolhan@usc.edu)
More informationThe many faces of ROC analysis in machine learning. Peter A. Flach University of Bristol, UK
The many faces of ROC analysis in machine learning Peter A. Flach University of Bristol, UK www.cs.bris.ac.uk/~flach/ Objectives After this tutorial, you will be able to [model evaluation] produce ROC
More informationMore Data Mining with Weka
More Data Mining with Weka Class 5 Lesson 1 Simple neural networks Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz Lesson 5.1: Simple neural networks Class
More informationSupervised Learning (Big Data Analytics)
Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used
More informationAn Introduction to Data Mining
An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail
More informationComparing the Results of Support Vector Machines with Traditional Data Mining Algorithms
Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms Scott Pion and Lutz Hamel Abstract This paper presents the results of a series of analyses performed on direct mail
More informationIntroduction to machine learning and pattern recognition Lecture 1 Coryn BailerJones
Introduction to machine learning and pattern recognition Lecture 1 Coryn BailerJones http://www.mpia.de/homes/calj/mlpr_mpia2008.html 1 1 What is machine learning? Data description and interpretation
More informationNeural Networks. Neural network is a network or circuit of neurons. Neurons can be. Biological neurons Artificial neurons
Neural Networks Neural network is a network or circuit of neurons Neurons can be Biological neurons Artificial neurons Biological neurons Building block of the brain Human brain contains over 10 billion
More informationExample: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation:  Feature vector X,  qualitative response Y, taking values in C
More informationKnowledge Discovery and Data Mining
Knowledge Discovery and Data Mining Lecture 15  ROC, AUC & Lift Tom Kelsey School of Computer Science University of St Andrews http://tom.home.cs.standrews.ac.uk twk@standrews.ac.uk Tom Kelsey ID505917AUC
More informationMS1b Statistical Data Mining
MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to
More informationIT Applications in Business Analytics SS2016 / Lecture 07 Use Case 1 (Two Class Classification) Thomas Zeutschler
Hochschule Düsseldorf University of Applied Scienses Fachbereich Wirtschaftswissenschaften W Business Analytics (M.Sc.) IT in Business Analytics IT Applications in Business Analytics SS2016 / Lecture 07
More informationAzure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Daybyday Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
More informationAn Introduction to Statistical Machine Learning  Overview 
An Introduction to Statistical Machine Learning  Overview  Samy Bengio bengio@idiap.ch Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP) CP 592, rue du Simplon 4 1920 Martigny, Switzerland
More informationArtificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing Email Classifier
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 22773878, Volume1, Issue6, January 2013 Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing
More informationIntroduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu
Introduction to Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction Logistics Prerequisites: basics concepts needed in probability and statistics
More informationHT2015: SC4 Statistical Data Mining and Machine Learning
HT2015: SC4 Statistical Data Mining and Machine Learning Dino Sejdinovic Department of Statistics Oxford http://www.stats.ox.ac.uk/~sejdinov/sdmml.html Bayesian Nonparametrics Parametric vs Nonparametric
More informationPrinciples of Data Mining by Hand&Mannila&Smyth
Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences
More informationRole of Neural network in data mining
Role of Neural network in data mining Chitranjanjit kaur Associate Prof Guru Nanak College, Sukhchainana Phagwara,(GNDU) Punjab, India Pooja kapoor Associate Prof Swami Sarvanand Group Of Institutes Dinanagar(PTU)
More informationAn Introduction to Machine Learning
An Introduction to Machine Learning L5: Novelty Detection and Regression Alexander J. Smola Statistical Machine Learning Program Canberra, ACT 0200 Australia Alex.Smola@nicta.com.au Tata Institute, Pune,
More informationNonnegative Matrix Factorization (NMF) in Semisupervised Learning Reducing Dimension and Maintaining Meaning
Nonnegative Matrix Factorization (NMF) in Semisupervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step
More informationA TUTORIAL. BY: Negin Yousefpour PhD Student Civil Engineering Department TEXAS A&M UNIVERSITY
ARTIFICIAL NEURAL NETWORKS: A TUTORIAL BY: Negin Yousefpour PhD Student Civil Engineering Department TEXAS A&M UNIVERSITY Contents Introduction Origin Of Neural Network Biological Neural Networks ANN Overview
More informationIntroduction to Neural Networks
Introduction to Neural Networks 2nd Year UG, MSc in Computer Science http://www.cs.bham.ac.uk/~jxb/inn.html Lecturer: Dr. John A. Bullinaria http://www.cs.bham.ac.uk/~jxb John A. Bullinaria, 2004 Module
More informationCSC 321 H1S Study Guide (Last update: April 3, 2016) Winter 2016
1. Suppose our training set and test set are the same. Why would this be a problem? 2. Why is it necessary to have both a test set and a validation set? 3. Images are generally represented as n m 3 arrays,
More informationLearning is a very general term denoting the way in which agents:
What is learning? Learning is a very general term denoting the way in which agents: Acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);
More informationAdvanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously
More informationPredicting the Risk of Heart Attacks using Neural Network and Decision Tree
Predicting the Risk of Heart Attacks using Neural Network and Decision Tree S.Florence 1, N.G.Bhuvaneswari Amma 2, G.Annapoorani 3, K.Malathi 4 PG Scholar, Indian Institute of Information Technology, Srirangam,
More informationSupport Vector Machines with Clustering for Training with Very Large Datasets
Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano
More informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationChapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining BecerraFernandez, et al.  Knowledge Management 1/e  2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
More information1. Classification problems
Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification
More informationMACHINE LEARNING IN HIGH ENERGY PHYSICS
MACHINE LEARNING IN HIGH ENERGY PHYSICS LECTURE #1 Alex Rogozhnikov, 2015 INTRO NOTES 4 days two lectures, two practice seminars every day this is introductory track to machine learning kaggle competition!
More informationNeural Networks and Support Vector Machines
INF5390  Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF539013 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines
More informationNeural Networks. Introduction to Artificial Intelligence CSE 150 May 29, 2007
Neural Networks Introduction to Artificial Intelligence CSE 150 May 29, 2007 Administration Last programming assignment has been posted! Final Exam: Tuesday, June 12, 11:302:30 Last Lecture Naïve Bayes
More informationEcommerce Transaction Anomaly Classification
Ecommerce Transaction Anomaly Classification Minyong Lee minyong@stanford.edu Seunghee Ham sham12@stanford.edu Qiyi Jiang qjiang@stanford.edu I. INTRODUCTION Due to the increasing popularity of ecommerce
More informationNeural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate Neural Traduction Automatique par Conjointement Apprentissage Pour Aligner et Traduire Dzmitry Bahdanau KyungHyun Cho Yoshua Bengio
More informationClassification of Bad Accounts in Credit Card Industry
Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition
More informationIntroduction to Data Mining
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Preprocessing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
More informationQuiz 1 for Name: Good luck! 20% 20% 20% 20% Quiz page 1 of 16
Quiz 1 for 6.034 Name: 20% 20% 20% 20% Good luck! 6.034 Quiz page 1 of 16 Question #1 30 points 1. Figure 1 illustrates decision boundaries for two nearestneighbour classifiers. Determine which one of
More informationData Clustering. Dec 2nd, 2013 Kyrylo Bessonov
Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms kmeans Hierarchical Main
More informationMetalearning. Synonyms. Definition. Characteristics
Metalearning Włodzisław Duch, Department of Informatics, Nicolaus Copernicus University, Poland, School of Computer Engineering, Nanyang Technological University, Singapore wduch@is.umk.pl (or search
More informationLecture 9: Introduction to Pattern Analysis
Lecture 9: Introduction to Pattern Analysis g Features, patterns and classifiers g Components of a PR system g An example g Probability definitions g Bayes Theorem g Gaussian densities Features, patterns
More informationNEURAL NETWORKS A Comprehensive Foundation
NEURAL NETWORKS A Comprehensive Foundation Second Edition Simon Haykin McMaster University Hamilton, Ontario, Canada Prentice Hall Prentice Hall Upper Saddle River; New Jersey 07458 Preface xii Acknowledgments
More informationUsing Neural Networks for Pattern Classification Problems
Using Neural Networks for Pattern Classification Problems Converting an Image Camera captures an image Image needs to be converted to a form that can be processed by the Neural Network Converting an Image
More informationNeural Networks. CAP5610 Machine Learning Instructor: GuoJun Qi
Neural Networks CAP5610 Machine Learning Instructor: GuoJun Qi Recap: linear classifier Logistic regression Maximizing the posterior distribution of class Y conditional on the input vector X Support vector
More informationPractical Introduction to Machine Learning and Optimization. Alessio Signorini <alessio.signorini@oneriot.com>
Practical Introduction to Machine Learning and Optimization Alessio Signorini Everyday's Optimizations Although you may not know, everybody uses daily some sort of optimization
More informationFoundations of Artificial Intelligence. Introduction to Data Mining
Foundations of Artificial Intelligence Introduction to Data Mining Objectives Data Mining Introduce a range of data mining techniques used in AI systems including : Neural networks Decision trees Present
More informationMachine learning for algo trading
Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with
More informationA Neural Support Vector Network Architecture with Adaptive Kernels. 1 Introduction. 2 Support Vector Machines and Motivations
A Neural Support Vector Network Architecture with Adaptive Kernels Pascal Vincent & Yoshua Bengio Département d informatique et recherche opérationnelle Université de Montréal C.P. 6128 Succ. CentreVille,
More informationAnalysis Tools and Libraries for BigData
+ Analysis Tools and Libraries for BigData Lecture 02 Abhijit Bendale + Office Hours 2 n Terry Boult (Waiting to Confirm) n Abhijit Bendale (Tue 2:45 to 4:45 pm). Best if you email me in advance, but I
More informationMaschinelles Lernen mit MATLAB
Maschinelles Lernen mit MATLAB Jérémy Huard Applikationsingenieur The MathWorks GmbH 2015 The MathWorks, Inc. 1 Machine Learning is Everywhere Image Recognition Speech Recognition Stock Prediction Medical
More informationComparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data
CMPE 59H Comparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Nonlinear
More information6. Feedforward mapping networks
6. Feedforward mapping networks Fundamentals of Computational Neuroscience, T. P. Trappenberg, 2002. Lecture Notes on Brain and Computation ByoungTak Zhang Biointelligence Laboratory School of Computer
More information