Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Save this PDF as:

Size: px
Start display at page:

Transcription

1 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1

2 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning 4. Different machine learning techniques (hypotheses) Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) 5. Practical usage and application: Pattern recognition 6. Conclusion and discussion 2

3 1. What is machine learning? Description: 1. Optimize a performance criterion using example data or past experience 2. Learning and discovering some rules or properties from the given data set 2-dimensional Feature space 3

4 1. What is machine learning Notation: (1) X = : (training) data set, which contains N samples, and each sample is an d-dim vector (2) Y = : (training) label set (3) Data pair: Each corresponds to a (4) Another form of data set: N = 1000 d = 2 4

5 1. What is machine learning? Is learning feasible? Data acquisition Practical usage Universal set (unobserved) Training set (observed) Testing set (unobserved) 5

6 1. What is machine learning? Is learning feasible? (1) Yes: in the probabilistic way by Hoeffding Inequality (2) No free lunch rule: Training set and testing set come from the same distribution Need to make some assumptions or bias (ex: classifier type, ) 6

7 1. What is machine learning Disciplines: Computer science and Statistics Statistics: Learning and Inference given samples Computer science: Efficient algorithms for optimization, and model representation & evaluation Two important factors: Modeling Optimization Other related disciplines: Neural networks, Pattern recognition, Information retrieval, Artificial Intelligence, Data mining, Function approximation, 7

8 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning 4. Different machine learning techniques (hypotheses) Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) 5. Practical usage and application: Pattern recognition 6. Conclusion and discussion 8

9 2. The basic of machine learning Design or learning Types of machine learning What re we seeking for? Machine learning structure (supervised) Optimization criterion Supervised learning strategies 9

10 2. The basic of machine learning Design or learning: Design: design some rules or strategies (If we know the intrinsic factors!) Learning: Learning from the (training) data Knowledge types: Domain knowledge Data-driven knowledge Example: feature generation or data compression (DCT, Wavelets VS. KL transform, PCA) 10

11 2. The basic of machine learning Design or learning Types of machine learning What re we seeking for? Machine learning structure (supervised) Optimization criterion Supervised learning strategies 11

12 2. The basic of machine learning Types of machine learning: Supervised learning ( ) (1) Prediction (2) Classification (discrete labels), Regression (real values) Unsupervised learning ( ) (1) Clustering (2) Probability distribution estimation (3) Finding association (in features) (4) Dimension reduction Reinforcement learning (1) Decision making (robot, chess machine) 12

13 2. The basic of machine learning Supervised learning Unsupervised learning What we learned Semi-supervised learning 13

14 2. The basic of machine learning Design or learning Types of machine learning What re we seeking for? Machine learning structure (supervised) Optimization criterion Supervised learning strategies 14

15 2. The basic of machine learning What re we seeking? (1) Supervised: Low E-out or maximize probabilistic terms E-in: for training set E-out: for testing set Hoeffding inequality and VC-bound tell us that minimizing E-in has some correspondences to reach low E-out With probability δ: About Model complexity (2) Unsupervised: Minimum quantization error, Minimum distance, MAP, MLE(maximum likelihood estimation) 15

16 2. The basic of machine learning Design or learning Types of machine learning What re we seeking for? Machine learning structure (supervised) Optimization criterion Supervised learning strategies 16

17 2. The basic of machine learning Machine learning structure: (Supervised) target: f Learning algorithm: A Stable: N, d Correctness Efficiency: fast Assumption, bias 17

18 2. The basic of machine learning Hypothesis set: (Linear classifier) Hypothesis set Through Learning algorithms 18

19 2. The basic of machine learning Design or learning Types of machine learning What re we seeking for? Machine learning structure (supervised) Optimization criterion Supervised learning strategies 19

20 2. The basic of machine learning Optimization criterion (for supervised linear classifier) Assume, where w is an d-dim vector (learned) +1-1 How to optimize with non-continuous error functions? For a sample with label 1: +1 error 1 Loss function -1 20

21 2. The basic of machine learning Optimization is a big issue in machine learning: Partial differential (Ex: convex optimization ) Genetic algorithm, Neural evolution Make differential available: Non-continuous (Piecewise) differentiable Approximation: (loss functions) VC bound Hypothesis + Loss function VC bound or generalization error 21

22 2. The basic of machine learning Approximation: Loss (objective) functions Different loss functions may find different final hypothesis For a sample with label 1: 1 error 0/1 loss (Perceptron learning) Logistic regression Hinge loss (SVM) Ada-line Square error (regression) 1 22

23 2. The basic of machine learning Optimization criterion: (1) Hypothesis type: linear classifier, decision tree, (2) Loss (objective) functions: Hinge loss, square error, (3) Optimization algorithms: (stable, efficient, correct) Gradient descent: back-propagation, general learning Convex optimization: primal & dual problems Dynamic programming: HMM Divide and Conquer: Decision tree induction, ** Hypothesis type affects model complexity ** Loss functions also affects model complexity & final hypothesis ** Algorithms find the desired hypothesis we want 23

24 2. The basic of machine learning Gradient descent: (searching for w) Loss (w) 24

25 2. The basic of machine learning Design or learning Types of machine learning What re we seeking for? Machine learning structure (supervised) Optimization criterion Supervised learning strategies 25

26 2. The basic of machine learning Supervised learning strategies: assume (1) One-shot (Discriminant model) (2) Two-stage (Probabilistic model) Discriminative: Generative: 26

27 2. The basic of machine learning What re we seeking? (revised) (1) One-shot (Discriminant) Low E-out by E-in or E-app target: f (2) Two-stage (Probabilistic model) Discriminative: Generative: 27

28 2. The basic of machine learning Supervised learning strategies: Comparison Category: One-shot Two-stage Model: Discriminant Generative, Discriminative Adv: Fewer assumptions Direct towards the goal More flexible Uncertainty & Domain-knowledge Discovery and analysis power Dis-Adv: No probabilistic signal More assumptions Computational complexity Usage: Supervised Supervised & Unsupervised Techniques: SVM, MLP, KNN, LDA, adaboost, CART, random forest GMM, GDA, Graphical models, HMM, Naïve Bayes 28

29 2. The basic of machine learning Machine learning revised: Theory: Goal: Methods: VC bound 29

30 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning 4. Different machine learning techniques(hypotheses) Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) 5. Practical usage and application: Pattern recognition 6. Conclusion and discussion 30

31 3. Principles and effects of machine learning Effect 1: General performance With probability δ: (VC bound, Generalization error) Factors: Complex model: Generative error: 31

32 3. Principles and effects of machine learning Model complexity: :Low :Middle :high 32

33 3. Principles and effects of machine learning Effect 2: N, VC dimension, and d on generalized error 33

34 3. Principles and effects of machine learning Effect 3: Under-fitting VS. Over-fitting (fixed N) error (model = hypothesis + loss functions) 34

35 3. Principles and effects of machine learning Effect 4: Bias VS. Variance (fixed N) (Frequently used in statistics regression) Bias: Ability to fit the training data (lower bias the better) Variance: Final hypothesis variance with the training set error Bias + Variance Variance Bias Universal set (unobserved) 35

36 3. Principles and effects of machine learning Effect 5: Learning curve (fixed ) error Generalized error N 36

37 3. Principles and effects of machine learning Principles: Occam s Razor: The simplest model that fits the data is also the most plausible! Sampling bias: If the data is sampled in a biased way, learning will produce a similarity biased outcome! Data snooping: If a data set has affected any step in the learning process, it cannot be fully trusted in assessing the outcome! 37

38 3. Principles and effects of machine learning Model selection: For a machine learning problems, we have many hypothesis, loss functions, and algorithms to try! 2 parameters for a hypothesis set: (1) Set parameter (manual), (2) Hypothesis parameter (learned) We need a method or strategy to find the best model (hypothesis set + loss function) for training and testing! ** Directly using the achieved training error (E-in) of each model for model selection is risky!! 38

39 3. Principles and effects of machine learning Model selection: Regularization: Validation: Training set Base set + Validation set (1)Train parameters on Base set (2)Test performance on Validation set 39

40 3. Principles and effects of machine learning Machine learning revised: Theory: Goal: Methods: VC bound 40

41 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning 4. Different machine learning techniques(hypotheses) Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) 5. Practical usage and application: Pattern recognition 6. Conclusion and discussion 41

42 4. Different machine learning techniques Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) Aggregation 42

43 4. Different machine learning techniques Linear classifier (numerical functions):, where w is an d-dim vector (learned) Techniques: Logistic regression, perceptron, Ada-line, Support vector machine (SVM), Multi-layer perceptron (MLP) Linear to nonlinear: Feature transform and Kernel 43

44 4. Different machine learning techniques Feature transform: Support vector machine (SVM): Useful because it combines regularization with feature transform 44

45 4. Different machine learning techniques Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) Aggregation 45

46 4. Different machine learning techniques Non-parametric (Instance-based functions): Techniques: (adaptive) K nearest neighbor, 46

47 4. Different machine learning techniques How large K is? About the model complexity Equal weight? Distance metric? 47

48 4. Different machine learning techniques Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) Aggregation 48

49 4. Different machine learning techniques Non-metric (Symbolic functions): Techniques: Decision tree, 49

50 4. Different machine learning techniques Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) Aggregation 50

51 4. Different machine learning techniques Parametric (Probabilistic functions): Techniques: Gaussian discriminant analysis (GDA), Naïve Bayes, Graphical models, P(x y = -1) P(x y = 1) 51

52 4. Different machine learning techniques Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) Aggregation 52

53 4. Different machine learning techniques Aggregation: (ensemble learning) Assume we have trained 5 models and get 5 final hypotheses For a test x g1(x) g2(x) g3(x) g4(x) g5(x) Final decision Techniques: Adaboost, Bagging, Random forest, 53

54 4. Different machine learning techniques Type Adv Dis-adv Techniques Linear low Too easy Logistic regression, Ada-line, perceptron learning rule, Linear + feature transform Hard to design transform functions 2 nd order polynomial transform, SVM Powerful Over-fitting Computational complexity C-SVM, ν-svm, SVR, SVM + RBF, SVM + poly, MLP Powerful Convergence? Back-propagation MLP Non-parametric Easy idea Computational complexity KNN, adaptive KNN, KNN with kernel smoother, Decision tree Easy idea Over-fitting CART, Parametric Flexible Discovery Computational complexity GDA, Naïve Bayes, Graphical models, PLSA, PCA, Aggregation Powerful Over-fitting Computational complexity Adaboost, Bagging, Random forest, Blending, 54

55 4. Different machine learning techniques Machine learning revised: Theory: Goal: Methods: VC bound Usage: Linear SVM, Adaboost, Random forest 55

56 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning 4. Different machine learning techniques (hypotheses) Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) 5. Practical usage and application: Pattern recognition 6. Conclusion and discussion 56

57 5. Practical usage and application Pattern recognition: Data base Test query Feature extraction Feature extraction Classifier training Results 57

58 5. Practical usage and application Pattern recognition: (1) Data pre-processing Raw data: music signal, image, Feature extraction: DCT, FT, Gabor feature, RGB, MFCC, Feature selection & feature transform: PCA, LDA, ICA, (2) Classifier training Choose a model and set parameters Train the classifier Test the performance, either with validation or regularization Find another model? 58

59 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning 4. Different machine learning techniques (hypotheses) Linear classifier (numerical functions) Non-parametric (Instance-based functions) Non-metric (Symbolic functions) Parametric (Probabilistic functions) 5. Practical usage and application: Pattern recognition 6. Conclusion and discussion 59

60 6. Conclusion and discussion Factors: Training set with N and d Assumptions: Hypothesis, loss functions, and same distribution Theory: (1) Cautions: (2) Probabilistic: Assume the basic distribution is known Over-fitting VS. Under-fitting Bias VS. Variance Learning curve 60

61 6. Conclusion and discussion Machine learning is powerful and widely-used in recent researches such as pattern recognition, data mining, and information retrieval Knowing how to use machine learning algorithms is very important Q&A? 61

62 Thank you for listening 62

CS 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

Learning 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);

Principles 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

BIOINF 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.

An 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

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines

An 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

CS 2750 Machine Learning. Lecture 1. Machine Learning. CS 2750 Machine Learning.

Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x-5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University

Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Models vs. Patterns Models A model is a high level, global description of a

Machine 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/machine-learning Logistics Lectures M 9:30-11:30 am Room 4419 Personnel

Introduction 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

Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones

Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones http://www.mpia.de/homes/calj/mlpr_mpia2008.html 1 1 What is machine learning? Data description and interpretation

KATE GLEASON COLLEGE OF ENGINEERING. John D. Hromi Center for Quality and Applied Statistics

ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM KATE GLEASON COLLEGE OF ENGINEERING John D. Hromi Center for Quality and Applied Statistics NEW (or REVISED) COURSE (KGCOE- CQAS- 747- Principles of

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

Neural Networks and Support Vector Machines

INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines

COLLEGE OF SCIENCE. John D. Hromi Center for Quality and Applied Statistics

ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE John D. Hromi Center for Quality and Applied Statistics NEW (or REVISED) COURSE: COS-STAT-747 Principles of Statistical Data Mining

Data, Measurements, Features

Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are

Understanding Network Traffic An Introduction to Machine Learning in Networking

Understanding Network Traffic An Introduction to Machine Learning in Networking Pedro CASAS FTW - Communication Networks Group Vienna, Austria 3rd TMA PhD School Department of Telecommunications AGH University

Supervised 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

INTRODUCTION TO NEURAL NETWORKS

INTRODUCTION TO NEURAL NETWORKS Pictures are taken from http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html http://research.microsoft.com/~cmbishop/prml/index.htm By Nobel Khandaker Neural Networks An

Learning. CS461 Artificial Intelligence Pinar Duygulu. Bilkent University, Spring 2007. Slides are mostly adapted from AIMA and MIT Open Courseware

1 Learning CS 461 Artificial Intelligence Pinar Duygulu Bilkent University, Slides are mostly adapted from AIMA and MIT Open Courseware 2 Learning What is learning? 3 Induction David Hume Bertrand Russell

A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization

A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization Ángela Blanco Universidad Pontificia de Salamanca ablancogo@upsa.es Spain Manuel Martín-Merino Universidad

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

Introduction to Machine Learning

Introduction to Machine Learning Prof. Alexander Ihler Prof. Max Welling icamp Tutorial July 22 What is machine learning? The ability of a machine to improve its performance based on previous results:

C19 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

Neural 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, 11:30-2:30 Last Lecture Naïve Bayes

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 - Multi-layer networks What is a Support

8/29/2015. Data Mining and Machine Learning. Erich Seamon University of Idaho

Data Mining and Machine Learning Erich Seamon University of Idaho www.webpages.uidaho.edu/erichs erichs@uidaho.edu 1 I am NOMAD 2 1 Data Mining and Machine Learning Outline Outlining the data mining and

Machine Learning and Data Analysis overview. Department of Cybernetics, Czech Technical University in Prague. http://ida.felk.cvut.

Machine Learning and Data Analysis overview Jiří Kléma Department of Cybernetics, Czech Technical University in Prague http://ida.felk.cvut.cz psyllabus Lecture Lecturer Content 1. J. Kléma Introduction,

Machine 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

Data Mining Practical Machine Learning Tools and Techniques

Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer

Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

Introduction to Data Mining

Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

HT2015: 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

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification

Social 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

Linear Models for Classification

Linear Models for Classification Sumeet Agarwal, EEL709 (Most figures from Bishop, PRML) Approaches to classification Discriminant function: Directly assigns each data point x to a particular class Ci

Data mining knowledge representation

Data mining knowledge representation 1 What Defines a Data Mining Task? Task relevant data: where and how to retrieve the data to be used for mining Background knowledge: Concept hierarchies Interestingness

Statistical Machine Learning

Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

Machine Learning for Data Science (CS4786) Lecture 1

Machine Learning for Data Science (CS4786) Lecture 1 Tu-Th 10:10 to 11:25 AM Hollister B14 Instructors : Lillian Lee and Karthik Sridharan ROUGH DETAILS ABOUT THE COURSE Diagnostic assignment 0 is out:

Model Combination. 24 Novembre 2009

Model Combination 24 Novembre 2009 Datamining 1 2009-2010 Plan 1 Principles of model combination 2 Resampling methods Bagging Random Forests Boosting 3 Hybrid methods Stacking Generic algorithm for mulistrategy

Learning outcomes. Knowledge and understanding. Competence and skills

Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

6.2.8 Neural networks for data mining

6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural

MACHINE LEARNING AN INTRODUCTION

AN INTRODUCTION JOSEFIN ROSÉN, SENIOR ANALYTICAL EXPERT, SAS INSTITUTE JOSEFIN.ROSEN@SAS.COM TWITTER: @ROSENJOSEFIN AGENDA What is machine learning? When, where and how is machine learning used? Exemple

EECS 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

Machine Learning: Overview

Machine Learning: Overview Why Learning? Learning is a core of property of being intelligent. Hence Machine learning is a core subarea of Artificial Intelligence. There is a need for programs to behave

Final Exam, Spring 2007

10-701 Final Exam, Spring 2007 1. Personal info: Name: Andrew account: E-mail address: 2. There should be 16 numbered pages in this exam (including this cover sheet). 3. You can use any material you brought:

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

Machine Learning and Data Mining. Fundamentals, robotics, recognition

Machine Learning and Data Mining Fundamentals, robotics, recognition Machine Learning, Data Mining, Knowledge Discovery in Data Bases Their mutual relations Data Mining, Knowledge Discovery in Databases,

1 1 Introduction Lecturer: Prof. Aude Billard (aude.billard@epfl.ch) Teaching Assistants: Guillaume de Chambrier, Nadia Figueroa, Denys Lamotte, Nicola Sommer 2 2 Course Format Alternate between: Lectures

Statistical Models in Data Mining

Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of

Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

Machine Learning. Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos)

Machine Learning Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos) What Is Machine Learning? A computer program is said to learn from experience E with respect to some class of

Review of some concepts in predictive modeling

Review of some concepts in predictive modeling Brigham and Women s Hospital Harvard-MIT Division of Health Sciences and Technology HST.951J: Medical Decision Support A disjoint list of topics? Naïve Bayes

Data 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

Linear Threshold Units

Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear

Learning. 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

Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval

Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

Learning outcomes. Knowledge and understanding. Ability and Competences. Evaluation capability and scientific approach

Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

Learning to Process Natural Language in Big Data Environment

CCF ADL 2015 Nanchang Oct 11, 2015 Learning to Process Natural Language in Big Data Environment Hang Li Noah s Ark Lab Huawei Technologies Part 1: Deep Learning - Present and Future Talk Outline Overview

Notes on Support Vector Machines

Notes on Support Vector Machines Fernando Mira da Silva Fernando.Silva@inesc.pt Neural Network Group I N E S C November 1998 Abstract This report describes an empirical study of Support Vector Machines

MACHINE LEARNING. Introduction. Alessandro Moschitti

MACHINE LEARNING Introduction Alessandro Moschitti Department of Computer Science and Information Engineering University of Trento Email: moschitti@disi.unitn.it Course Schedule Lectures Tuesday, 14:00-16:00

Data Mining Practical Machine Learning Tools and Techniques. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Engineering the input and output Attribute selection Scheme independent, scheme

203.4770: 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:

Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning

Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step

Classification: Basic Concepts, Decision Trees, and Model Evaluation. General Approach for Building Classification Model

10 10 Classification: Basic Concepts, Decision Trees, and Model Evaluation Dr. Hui Xiong Rutgers University Introduction to Data Mining 1//009 1 General Approach for Building Classification Model Tid Attrib1

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.

(International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models

NEURAL 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

Analysis of kiva.com Microlending Service! Hoda Eydgahi Julia Ma Andy Bardagjy December 9, 2010 MAS.622j

Analysis of kiva.com Microlending Service! Hoda Eydgahi Julia Ma Andy Bardagjy December 9, 2010 MAS.622j What is Kiva? An organization that allows people to lend small amounts of money via the Internet

What is Learning? CS 391L: Machine Learning Introduction. Raymond J. Mooney. Classification. Problem Solving / Planning / Control

What is Learning? CS 391L: Machine Learning Introduction Herbert Simon: Learning is any process by which a system improves performance from experience. What is the task? Classification Problem solving

Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors

Classification k-nearest neighbors Data Mining Dr. Engin YILDIZTEPE Reference Books Han, J., Kamber, M., Pei, J., (2011). Data Mining: Concepts and Techniques. Third edition. San Francisco: Morgan Kaufmann

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING

Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction

MA2823: Foundations of Machine Learning

MA2823: Foundations of Machine Learning École Centrale Paris Fall 2015 Chloé-Agathe Azencot Centre for Computational Biology, Mines ParisTech chloe agathe.azencott@mines paristech.fr TAs: Jiaqian Yu jiaqian.yu@centralesupelec.fr

Training Methods for Adaptive Boosting of Neural Networks for Character Recognition

Submission to NIPS*97, Category: Algorithms & Architectures, Preferred: Oral Training Methods for Adaptive Boosting of Neural Networks for Character Recognition Holger Schwenk Dept. IRO Université de Montréal

TIETS34 Seminar: Data Mining on Biometric identification

TIETS34 Seminar: Data Mining on Biometric identification Youming Zhang Computer Science, School of Information Sciences, 33014 University of Tampere, Finland Youming.Zhang@uta.fi Course Description Content

Using Data Mining for Mobile Communication Clustering and Characterization

Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer

A Simple Introduction to Support Vector Machines

A Simple Introduction to Support Vector Machines Martin Law Lecture for CSE 802 Department of Computer Science and Engineering Michigan State University Outline A brief history of SVM Large-margin linear

Classifiers & 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

LCs for Binary Classification

Linear Classifiers A linear classifier is a classifier such that classification is performed by a dot product beteen the to vectors representing the document and the category, respectively. Therefore it

Big Data and Marketing

Big Data and Marketing Professor Venky Shankar Coleman Chair in Marketing Director, Center for Retailing Studies Mays Business School Texas A&M University http://www.venkyshankar.com venky@venkyshankar.com

Introduction to Neural Networks : Revision Lectures

Introduction to Neural Networks : Revision Lectures John A. Bullinaria, 2004 1. Module Aims and Learning Outcomes 2. Biological and Artificial Neural Networks 3. Training Methods for Multi Layer Perceptrons

MACHINE 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!

Introduction to Statistical Machine Learning

CHAPTER Introduction to Statistical Machine Learning We start with a gentle introduction to statistical machine learning. Readers familiar with machine learning may wish to skip directly to Section 2,

The Artificial Prediction Market

The Artificial Prediction Market Adrian Barbu Department of Statistics Florida State University Joint work with Nathan Lay, Siemens Corporate Research 1 Overview Main Contributions A mathematical theory

Why Ensembles Win Data Mining Competitions

Why Ensembles Win Data Mining Competitions A Predictive Analytics Center of Excellence (PACE) Tech Talk November 14, 2012 Dean Abbott Abbott Analytics, Inc. Blog: http://abbottanalytics.blogspot.com URL:

10-601 Machine Learning http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/index.html Course data All up-to-date info is on the course web page: http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/index.html

MS1b 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

Simple and efficient online algorithms for real world applications

Simple and efficient online algorithms for real world applications Università degli Studi di Milano Milano, Italy Talk @ Centro de Visión por Computador Something about me PhD in Robotics at LIRA-Lab,

Machine Learning. Term 2012/2013 LSI - FIB. Javier Béjar cbea (LSI - FIB) Machine Learning Term 2012/2013 1 / 34

Machine Learning Javier Béjar cbea LSI - FIB Term 2012/2013 Javier Béjar cbea (LSI - FIB) Machine Learning Term 2012/2013 1 / 34 Outline 1 Introduction to Inductive learning 2 Search and inductive learning

A Review of Data Mining Techniques

Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,

Decompose Error Rate into components, some of which can be measured on unlabeled data

Bias-Variance Theory Decompose Error Rate into components, some of which can be measured on unlabeled data Bias-Variance Decomposition for Regression Bias-Variance Decomposition for Classification Bias-Variance

Content-Based Recommendation

Content-Based Recommendation Content-based? Item descriptions to identify items that are of particular interest to the user Example Example Comparing with Noncontent based Items User-based CF Searches