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See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/303806260 Machine Learning: Algorithms and Applications Book · July 2016 DOI: 10.1201/9781315371658 CITATIONS 264READS 183,431 3 author s, including: Mohssen M. Z. E. Mohammed Al-Imam Muhammad bin Saud Islamic Univ ersity 27 PUBLICA TIONS    481 CITATIONS     SEE PROFILE Eihab Bashier Mohammed Bashier Sohar Univ ersity 61 PUBLICA TIONS    644 CITATIONS     SEE PROFILE All c ontent f ollo wing this p age was uplo aded b y Eihab Bashier Mohammed Bashier on 27 Dec ember 2016.

Discuss the significance of ED ENHANC EMENT OF THE DO WNLO.

The user has r equest ed enhanc ement of the do wnlo aded file.Adaptive, Dynamic, and Resilient SystemsEdited by Niranjan Suri and Giacomo CabriISBN 978-1-4398-6848-5 Anti-Spam Techniques Based on Artificial Immune SystemYing TanISBN 978-1-4987-2518-7 Case Studies in Secure Computing: Achievements and TrendsEdited by Biju Issac and Nauman IsrarISBN 978-1-4822-0706-4 Cognitive Robotics Edited by Hooman SamaniISBN 978-1-4822-4456-4 Computational Intelligent Data Analysis for Sustainable DevelopmentEdited by Ting Yu, Nitesh Chawla, and Simeon SimoffISBN 978-1-4398-9594-8 Computational Trust Models and Machine LearningXin Liu, Anwitaman Datta, and Ee-Peng LimISBN 978-1-4822-2666-9 Enhancing Computer Security with Smart TechnologyV.

Explain in detail RAO VEMURIISBN.

Rao VemuriISBN 978-0-8493-3045-2Exploring Neural Networks with C#Ryszard Tadeusiewicz, Rituparna Chaki, and Nabendu ChakiISBN 978-1-4822-3339-1 Generic and Energy-Efficient Context-Aware Mobile SensingOzgur Yurur and Chi Harold LiuISBN 978-1-4987-0010-8 Network Anomaly Detection: A Machine Learning PerspectiveDhruba Kumar Bhattacharyya and Jugal Kumar KalitaISBN 978-1-4665-8208-8 Risks of Artificial Intelligence Vincent C. MüllerISBN 978-1-4987-3482-0 The Cognitive Early Warning Predictive System Using the Smart Vaccine: The New Digital Immunity Paradigm for Smart Cities and Critical InfrastructureRocky TermaniniISBN 978-1-4987-2651-1 The State of the Art in Intrusion Prevention and DetectionEdited by Al-Sakib Khan PathanISBN 978-1-4822-0351-6 Zeroing Dynamics, Gradient Dynamics, and Newton IterationsYunong Zhang, Lin Xiao, Zhengli Xiao, and Mingzhi MaoISBN 978-1-4987-5376-0OTHER TITLES FROM AUERBACH PUBLICATIONS AND CRC PRESS Machine Learning Algorithms and Applications Mohssen Mohammed Muhammad Badruddin Khan Eihab Bashier Mohammed Bashier Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCMATLAB® is a trademark of The MathWorks, Inc. and is used with permission.

What do you mean by BOOK ’.

This book’s use or discussion of MATLAB® soft - ware or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software.

Explain in detail CRC PRESSTAYLOR.

CRC PressTaylor & Francis Group6000 Broken Sound Parkway NW, Suite 300Boca Raton, FL 33487-2742 © 2017 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government worksPrinted on acid-free paper Version Date: 20160428 International Standard Book Number-13: 978-1-4987-0538-7 (Hardback)This book contains information obtained from authentic and highly regarded sources.

Elaborate on RESPONSIBILITY FOR THE VALIDITY.

Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use.

What are the main points of PLEASE WRITE.

If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.

Explain in detail COPYRIGHT LAW.

Copyright Law, no part of this book may be reprinted, reproduced, transmit - ted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers.

How would you explain COPYRIGHT CLEARANCE CENTER.

com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400.

Explain in detail PHOTOCOPY LICENSE.

For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged.

Summarize TRADEMARK NOTICE.

Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Summarize MACHINE LEARNING.

Title: Machine learning : algorithms and applications / Mohssen Mohammed, Muhammad Badruddin Khan, and Eihab Bashier Mohammed Bashier.Description: Boca Raton : CRC Press, 2017.

Elaborate on COMPUTER.

| Computer algorithms.Classification: LCC Q325.5 .M63 2017 | DDC 006.3/12 --dc23 LC record available at https://lccn.loc.gov/2016015290 Visit the Taylor & Francis Web site athttp://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCviiContents Preface ................................................................................ xiii Acknowledgments ............................................................. xv Authors .............................................................................. xvii Introduction ...................................................................... xix 1 Introduction to Machine Learning...........................1 1.1 Introduction ................................................................ 1 1.2 Preliminaries ............................................................... 2 1.2.1 Machine Learning: Where Several Disciplines Meet ............................................... 4 1.2.2 Supervised Learning ........................................ 7 1.2.3 Unsupervised Learning .................................... 9 1.2.4 Semi-Supervised Learning .............................. 10 1.2.5 Reinforcement Learning .................................. 11 1.2.6 Validation and Evaluation ............................... 11 1.3 Applications of Machine Learning Algorithms ......... 14 1.3.1 Automatic Recognition of Handwritten Postal Codes .................................................... 15 1.3.2 Computer-Aided Diagnosis ............................. 17 1.3.3 Computer Vision ............................................. 19 1.3.3.1 Driverless Cars .................................... 20 1.3.3.2 Face Recognition and Security ........... 22 1.3.4 Speech Recognition ........................................ 22 Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCviii ◾ Contents 1.3.5 Text Mining ..................................................... 23 1.3.5.1 Where Text and Image Data Can Be Used Together ............................... 24 1.4 The Present and the Future ...................................... 25 1.4.1 Thinking Machines ......................................... 25 1.4.2 Smart Machines .............................................. 28 1.4.3 Deep Blue ....................................................... 30 1.4.4 IBM’s Watson .................................................. 31 1.4.5 Google Now .................................................... 32 1.4.6 Apple’s Siri ...................................................... 32 1.4.7 Microsoft’s Cortana ......................................... 32 1.5 Objective of This Book ............................................. 33 References .......................................................................... 34 SeCtion i SUPeRViSeD LeARninG ALGoRitHMS 2 Decision Trees ....................................................... 37 2.1 Introduction ............................................................... 37 2.2 Entropy ...................................................................... 38 2.2.1 Example .......................................................... 38 2.2.2 Understanding the Concept of Number of Bits .............................................................. 40 2.3 Attribute Selection Measure ...................................... 41 2.3.1 Information Gain of ID3................................. 41 2.3.2 The Problem with Information Gain ............ 44 2.4 Implementation in MATLAB® .................................. 46 2.4.1 Gain Ratio of C4.5 .......................................... 49 2.4.2 Implementation in MATLAB .......................... 51 References .......................................................................... 52 3 Rule-Based Classifiers ............................................ 53 3.1 Introduction to Rule-Based Classifiers ...................... 53 3.2 Sequential Covering Algorithm .................................54 3.3 Algorithm ...................................................................543.4 Visualization .............................................................. 55 3.5 Ripper ........................................................................ 55 3.5.1 Algorithm ........................................................ 56 Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCContents ◾ ix 3.5.2 Understanding Rule Growing Process ........... 58 3.5.3 Information Gain ............................................ 65 3.5.4 Pruning ............................................................ 66 3.5.5 Optimization .................................................. 68 References .......................................................................... 72 4 Naïve Bayesian Classification................................. 73 4.1 Introduction ............................................................... 73 4.2 Example ..................................................................... 74 4.3 Prior Probability ........................................................ 75 4.4 Likelihood .................................................................. 75 4.5 Laplace Estimator ...................................................... 77 4.6 Posterior Probability .................................................. 78 4.7 MATLAB Implementation ......................................... 79 References .......................................................................... 82 5 The k -Nearest Neighbors Classifiers ...................... 83 5.1 Introduction ............................................................... 83 5.2 Example .................................................................... 84 5.3 k-Nearest Neighbors in MATLAB® ........................... 86 References ......................................................................... 88 6 Neural Networks .................................................... 89 6.1 Perceptron Neural Network ......................................89 6.1.1 Perceptrons .................................................... 90 6.2 MATLAB Implementation of the Perceptron Training and Testing Algorithms ..............................94 6.3 Multilayer Perceptron Networks .............................. 96 6.4 The Backpropagation Algorithm .............................. 99 6.4.1 Weights Updates in Neural Networks .......... 101 6.5 Neural Networks in MATLAB ................................. 102 References ........................................................................ 105 7 Linear Discriminant Analysis .............................. 107 7.1 Introduction ............................................................. 107 7.2 Example ................................................................... 108 References ........................................................................ 114 Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCx ◾ Contents 8 Support Vector Machine ...................................... 115 8.1 Introduction ........................................................... 115 8.2 Definition of the Problem ..................................... 116 8.2.1 Design of the SVM .................................... 120 8.2.2 The Case of Nonlinear Kernel .................. 126 8.3 The SVM in MATLAB® .......................................... 127 References ........................................................................ 128 SeCtion ii UnSUPeRViSeD LeARninG ALGoRitHMS 9 k-Means Clustering .............................................. 131 9.1 Introduction ........................................................... 131 9.2 Description of the Method .................................... 132 9.3 The k-Means Clustering Algorithm ....................... 133 9.4 The k-Means Clustering in MATLAB® .................. 134 10 Gaussian Mixture Model ...................................... 137 10.1 Introduction ........................................................... 137 10.2 Learning the Concept by Example ....................... 138 References ........................................................................ 143 11 Hidden Markov Model ......................................... 145 11.1 Introduction ........................................................... 145 11.2 Example ................................................................. 146 11.3 MATLAB Code ...................................................... 148 References ........................................................................ 152 12 Principal Component Analysis ............................. 153 12.1 Introduction ........................................................... 153 12.2 Description of the Problem ................................... 154 12.3 The Idea behind the PCA ..................................... 155 12.3.1 The SVD and Dimensionality Reduction .............................................. 157 12.4 PCA Implementation ............................................. 158 12.4.1 Number of Principal Components to Choose .................................................. 159 12.4.2 Data Reconstruction Error ........................ 160 Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCContents ◾ xi 12.5 The Following MATLAB® Code Applies the PCA ............................................................... 161 12.6 Principal Component Methods in Weka ............... 163 12.7 Example: Polymorphic Worms Detection Using PCA .............................................................. 167 12.7.1 Introduction ............................................... 167 12.7.2 SEA, MKMP, and PCA ............................... 168 12.7.3 Overview and Motivation for Using String Matching ......................................... 169 12.7.4 The KMP Algorithm .................................. 170 12.7.5 Proposed SEA ............................................ 171 12.7.6 An MKMP Algorithm ................................ 173 12.7.6.1 Testing the Quality of the Generated Signature for Polymorphic Worm A ................. 174 12.7.7 A Modified Principal Component Analysis ..................................................... 174 12.7.7.1 Our Contributions in the PCA ..... 174 12.7.7.2 Testing the Quality of Generated Signature for Polymorphic Worm A ................. 178 12.7.7.3 Clustering Method for Different Types of Polymorphic Worms ..... 179 12.7.8 Signature Generation Algorithms Pseudo-Codes ............................................ 179 12.7.8.1 Signature Generation Process ..... 180 References ........................................................................ 187 Appendix I: Transcript of Conversations with Chatbot ........................................... 189 Appendix II: Creative Chatbot .................................... 193 Index .......................................................................... 195 Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCxixintroduction Since their evolution, humans have been using many types of tools to accomplish various tasks.

Explain in detail MACHINE INDUSTRY.

Despite rapid developments in the machine industry, intel- ligence has remained the fundamental difference between humans and machines in performing their tasks.

What are the applications of INFORMATION FROM THE SUR-ROUNDING ATMOSPHERE.

A human uses his or her senses to gather information from the sur-rounding atmosphere; the human brain works to analyze that information and takes suitable decisions accordingly.

How would you explain STORY OF HARRY POTTER.

For example, a machine is not expected to understand the story of Harry Potter, jump over a hole in the street, or interact with other machines through a common language.

What do you mean by ALAN TURING.

The era of intelligent machines started in the mid-twentieth century when Alan Turing thought whether it is possible for machines to think.

Explain in detail SCIENCE FICTION.

Many science fiction movies have expressed these dreams, such as Artificial Intelligence ; The Matrix ; The Terminator ; I, Robot ; and Star Wars .

Elaborate on CLICK.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCxx ◾ Introduction The history of AI started in the year 1943 when Waren McCulloch and Walter Pitts introduced the first neural network model.

Explain in detail WORK IN THE DEVELOPMENT.

Alan Turing introduced the next noticeable work in the development of the AI in 1950 when he asked his famous question: can machines think?

How would you explain JOHN MCCARTHY.

In 1956, John McCarthy, Marvin Minsky, Nathan Rochester of IBM, and Claude Shannon organized the first summer AI conference at Dartmouth College, the United States.

How would you explain SYMPOSIUM IN INFORMATION SCIENCE.

The term cognitive science originated in 1956, during a symposium in information science at the MIT, the United States.

Explain in detail NATIONAL RESEARCH COUNCIL.

The National Research Council (NRC) of the United States founded the Automatic Language Processing Advisory Committee (ALPAC) in 1964 to advance the research in the natural language pro - cessing.

Explain in detail MARVIN MINSKY.

Marvin Minsky and Seymour Papert published their book Perceptrons in 1969, in which they demonstrated the limita- tions of neural networks.

Define DAVID RUMELHART.

However, in 1986, David Rumelhart, Geoffrey Hinton, and Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction ◾ xxi Ronald Williams discovered a method that allowed a network to learn to discriminate between nonlinear separable classes, and they named it backpropagation .

What are the main points of JOHN H. HOLLAND.

In 1987, John H. Holland and Arthur W. Burks invented an adapted computing system that is capable of learning.

What are the main points of DEVELOPMENT OF THE THEORY.

In fact, the development of the theory and application of genetic algorithms was inspired by the book Adaptation in Neural and Artificial Systems , written by Holland in 1975.

What are the applications of DEAN POMERLEAU.

In 1989, Dean Pomerleau proposed ALVINN (autonomous land vehicle in a neural network), which was a three-layer neural network designed for the task of the road following.

What are the main points of DEEP BLUE.

In the year 1997, the Deep Blue chess machine, designed by IBM, defeated Garry Kasparov, the world chess champion.

Describe the process of WATSON.

In 2011, Watson, a computer developed by IBM, defeated Brad Rutter and Ken Jennings, the champions of the television game show Jeopardy!

Describe the process of REINFORCEMENT LEARNING.

The period from 1997 to the present witnessed rapid devel- opments in reinforcement learning, natural language process-ing, emotional understanding, computer vision, and computer hearing.

Describe the process of RESEARCH IN MACHINE LEARNING.

The current research in machine learning focuses on com- puter vision, hearing, natural languages processing, image processing and pattern recognition, cognitive computing, knowledge representation, and so on.

Summarize ALGORITHMS.

The objectives of machine learning are to enable Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCxxii ◾ Introduction machines to make predictions, perform clustering, extract association rules, or make decisions from a given dataset.

What are the applications of APPLICATIONS.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC1Chapter 1 Introduction to Machine Learning 1.1 Introduction Learning is a very personalized phenomenon for us.

Describe the process of PHILOSOPHY.

Will Durant in his famous book, The Pleasures of Philosophy , won - dered in the chapter titled “Is Man a Machine?” when he wrote such classical lines: Here is a child; … See it raising itself for the first time, fearfully and bravely, to a vertical dignity; why should it long so to stand and walk?

Discuss the significance of ] NEVERTHELESS.

Why should it tremble with perpetual curiosity, with perilous and insatiable ambition, touching and tasting, watch-ing and listening, manipulating and experimenting, observing and pondering, growing —till it weighs the earth and charts and measures the stars?… [1] Nevertheless, learning is not limited to humans only.

Describe the process of INTERNATIONAL STANDARD BOOK NUMBER-13.

Plants also show intelligent Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC2 ◾ Machine Learning behavior.

What are the main points of ILLUSION OF FREEDOM.

Enabling a machine capable of learning like humans is a dream, the fulfillment of which can lead us to having deterministic machines with freedom (or illusion of freedom in a sense).

What are the applications of TRAFFIC FLOW.

Initially, machines were designed to perform specific tasks, such as running on the railway, controlling the traffic flow, digging deep holes, traveling into the space, and shooting at moving objects.

Elaborate on PERCEPTION PROCESS.

In the perception process, the data is organized, recognized by comparing it to previous experiences that were stored in the memory, and interpreted.

What do you mean by HARDBACK.

It does not have the ability to analyze data for Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 3 classification, benefit from previous experiences, and store the new experiences to the memory units; that is, machines do not learn from experience.

Describe the process of PLAY ROMEO.

Although machines are expected to do mechanical jobs much faster than humans, it is not expected from a machine to: understand the play Romeo and Juliet , jump over a hole in the street, form friendships, interact with other machines through a common language, recognize dangers and the ways to avoid them, decide about a disease from its symp - toms and laboratory tests, recognize the face of the criminal, and so on.

Explain in detail QUESTION OF WHETHER A MACHINE.

The question of whether a machine can think was first asked by the British mathematician Alan Turing in 1955, which was the start of the artificial intelligence history.

What is the importance of TURING.

Section 1.4 also discusses the progress that has been achieved in determining whether our machines can pass the Turing test.

Describe the process of TAYLOR.

These meth- ods or algorithms are basically a sequence of instructions Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC4 ◾ Machine Learning that are executed to reach from one state to another in order to produce output from input.

How would you explain SET OF INPUT.

For example, if two programs are made based on two different algorithms to find the smallest number in an unordered list, then for the same list of unordered number (or same set of input) and on the same machine, one measure of efficiency can be speed or quickness of program and another can be minimum memory usage.

What do you mean by REDUCTION IN MEMORY USAGE.

In some situations, time and space can be inter-related, that is, the reduction in memory usage leading to fast execution of the algorithm.

Summarize CACHE MEMORY.

For example, an efficient algorithm enabling a program to handle full input data in cache memory will also consequently allow faster execution of program.

How would you explain DISCIPLINES MEET MACHINE.

1.2.1 Machine Learning: Where Several Disciplines Meet Machine learning is a branch of artificial intelligence that aims at enabling machines to perform their jobs skillfully by using intelligent software.

What are the applications of KNOWLEDGE DISCOVERY.

Similarly, there are familiar terms such as Knowledge Discovery from Data (KDD), data mining, and pattern recognition.

What is the importance of SAS INSTITUTE INC..

SAS Institute Inc., North Carolina, is a developer of the famous analytical software Statistical Analysis System (SAS).

Define CONNECTION OF THE DISCIPLINE.

In order to show the connection of the discipline of machine learning with different related disciplines, we will use the illus - tration from SAS.

What are the main points of DATA MINING COURSE.

This illustration was actually used in a data mining course that was offered by SAS in 1998 (see Figure 1.1).

What are the main points of FRANCIS GROUP.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 5 In a 2006 article entitled “The Discipline of Machine Learning,” Professor Tom Mitchell [3, p.1] defined the discipline of machine learning in these words: Machine Learning is a natural outgrowth of the intersection of Computer Science and Statistics.

What are the main points of QUESTION OF COMPUTER SCIENCE.

We might say the defining question of Computer Science is ‘How can we build machines that solve problems, and which problems are inherently tractable/intractable?’ The question that largely defines Statistics is ‘What can be inferred from data plus a set of modeling assumptions, with what reli-ability?’ The defining question for Machine Learning builds on both, but it is a distinct question.

Define WHEREAS COMPUTER SCIENCE.

Whereas Computer Science has focused primarily on how to manually program computers, Machine Learning focuses on the question of how to get comput-ers to program themselves (from experience plus some initial structure).

Elaborate on KDDPATTER.

Whereas Statistics Statistics KDDPatter n recognitionNeuroc omputing AI Database sMachine learning Data mining Figure 1.1 Different disciplines of knowledge and the discipline of machine learning.

Describe the process of GUTHRIE.

(From Guthrie, Looking backwards, looking forwards: SAS, data mining and machine learning, 2014, http://blogs.sas.com/ content/subconsciousmusings/2014/08/22/looking-backwards-looking-forwards-sas-data-mining-and-machine-learning/2014.

What are the applications of LLC6 ◾ MACHINE LEARNING.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC6 ◾ Machine Learning has focused primarily on what conclusions can be inferred from data, Machine Learning incorporates additional questions about what computational architectures and algorithms can be used to most effectively capture, store, index, retrieve and merge these data, how multiple learning subtasks can be orchestrated in a larger system, and questions of computational tractability [emphasis added].

How would you explain RECOGNITION IN THIS CASE.

Because of the lack of understanding of such phenomenon (speech recognition in this case), we cannot craft algorithms for such scenarios.

What are the main points of HENCE.

Hence, another measure of performance (besides performance of metrics of speed and memory usage) of a machine learning algorithm will be the accuracy of results.

Explain in detail STATEMENT ABOUT LEARNING.

It seems appropriate here to quote another statement about learning of computer program from Professor Tom Mitchell from Carnegie Mellon University [4, p.2]: Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 7 A computer program is said to learn from experi- ence E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. The subject will be further clarified when the issue will be discussed with examples at their relevant places.

Describe the process of DATA MINING COMMUNITY.

However, before the discussion, a few widely used terminologies in the machine learning or data mining community will be discussed as a prerequisite to appreciate the examples of machine learning applications.

How would you explain LEARNING.

1.2.2 Supervised Learning In supervised learning, the target is to infer a function or mapping from training data that is labeled .

Summarize TAG FROM VECTOR.

A label or tag from vector Y is the explanation of its respec- tive input example from input vector X .

What do you mean by MACHINE.

Together they form Machine learning techniques Supervised learning Concerne d with classified (labeled) dataConcerne d with uncla ssified (unlab eled) dataConcerne d with mixture of classified and uncla ssifie d dataNo dataUnsuper vised learningReinforcement learningSemi-super vised learning Figure 1.2 Different machine learning techniques and their required data.

What is the importance of LLC8 ◾ MACHINE.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC8 ◾ Machine Learning a training example .

What are the applications of COLUMN OF THE TABLE.

The second column of the table titled, “Example judg- ment for labeling” expresses possible criterion for each data example.

Summarize SENTIMENT ANALYSIS.

Sentiment analysis, image recognition, and speech detection technologies have made progress in past three decades but there is still a lot of room for improvement before we can equate them with humans’ performance.

Define CASE OF TUMOR DETECTION.

In the fifth case of tumor detection, even normal humans can-not label the X-ray data, and expensive experts’ services are required for such labeling.

What are the applications of CLASSIFICATION CLICK.

Classification Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 9 1.2.3 Unsupervised Learning In unsupervised learning, we lack supervisors or training data.

Describe the process of MACHINE LEARNING PRACTITIONER.

The situation faced by a machine learning practitioner is somehow similar to the scene described in Alice’s Adventures in Wonderland [5, p.100], an 1865 novel, when Alice looking to go somewhere , talks to the Cheshire cat.Table 1.1 Unlabeled Data Examples along with Labeling Issues Unlabeled Data ExampleExample Judgment for LabelingPossible LabelsPossible Supervisor Tweet Sentiment of the tweetPositive/ negativeHuman/ machine Photo Contains house and carYes/No Human/ machine Audio recordingThe word football is utteredYes/No Human/ machine Video Are weapons used in the video?Violent/ nonviolentHuman/ machine X-ray Tumor presence in X-rayPresent/ absentExperts/ machine Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC10 ◾ Machine Learning … She went on.

What are the applications of ”.

“Would you tell me, please, which way I ought to go from here?” “That depends a good deal on where you want to get to,” said the Cat.

What are the applications of “ OH.

“Oh, you’re sure to do that,” said the Cat, “if you only walk long enough .” In the machine learning community, probably clustering (an unsupervised learning algorithm) is analogous to the walk long enough instruction of the Cheshire cat.

Elaborate on TYPE OF LEARNING.

1.2.4 Semi-Supervised Learning In this type of learning, the given data are a mixture of classified and unclassified data.

Define LLCINTRODUCTION.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 11 2.

Define REINFORCEMENT.

1.2.5 Reinforcement Learning The reinforcement learning method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk.

Summarize DECISION MAKING.

Using the stored information, policy for particular state in terms of action can be fine-tuned, thus helping in optimal decision making for our agent.

Define VALIDATION.

1.2.6 Validation and Evaluation Assessing whether the model learnt from machine learning algorithm is good or not, needs both validation and evaluation.

Discuss the significance of PLATO CLICK.

However, before discussing these two important terminologies, it is interesting to mention the writings of Plato Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC12 ◾ Machine Learning (the great philosopher) regarding this issue.

What do you mean by BOX.

BOX 1.1 PLATO ON STABILITY OF BELIEF Plato’s ethics is written by Terence Irwin, professor of the history of philosophy at the University of Oxford.

Describe the process of “ KNOWLEDGE.

In sec - tion 101 titled “Knowledge, Belief and Stability,” there is an interesting debate about wandering of beliefs.

Summarize PLATO.

Plato also needs to consider the different circumstance that might cause true beliefs to wander away … Different demands for stability might rest on different standards of reliability.

How would you explain DIFFERENCE BETWEEN SHEEP.

If, however, I cannot tell the difference between sheep and goat and do not know why these animals are sheep rather than goats, my ignorance would make a difference if I were confronted with goats.

What are the applications of ‘.

If we are concerned about ‘empirical reliability’ (the tendency to be right in empirically likely conditions), my belief that animals with a certain appearance are sheep may be perfectly reliable (if I can be expected not to meet any goats).

Describe the process of RELIABILITY ’.

If we are concerned about ‘counterfactual reliability’ (the tendency to be right in counterfactual, and not necessarily empirically likely, conditions), my inability to distinguish sheep from goats make (Continued ) Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 13 If one claims that for a particular training data the function fits perfectly, then for the machine learning community, this claim is not enough.

Write a short note on PERFORMANCE ON TRAINING.

Sometimes, it is the phenomenon of overfitting that will give best performance on training data, and when BOX 1.1 (CONTINUED) PLATO ON STABILITY OF BELIEF my belief unreliable that animals with certain appearance are sheep.

Describe the process of BELIEF ABOUT SHEEP.

In saying that my belief about sheep is counterfactually unreliable, we point out that my reason for believing that these things are sheep is mistaken, even though the mistake makes no difference to my judgements in actual circumstances.

Describe the process of BELIEF ‘.

When Plato speaks of a given belief ‘wandering’, he describes a fault that we might more easily recognize if it were described differently.

How would you explain ’ IN AN ENVIRONMENT.

If I identify sheep by features that do not distinguish them from goats, then I rely on false principles to reach the true belief ‘this is a sheep’ in an environment without goats.

Describe the process of SHEEP IN AN ENVIRONMENT.

If I rely on the same principles to identify sheep in an environment that includes goats, I will often reach the false belief ‘this is a sheep ‘when I meet a goat.

Define TOKEN BELIEF ‘.

We may want to describe these facts by speaking of three things: (1) the true token belief ‘this is a sheep’ (applied to a sheep in the first environment), (2) the false token belief ‘this is a sheep’ (applied to a goat in the second environment), and (3) the false general principle that I use to identify sheep in both environments.

What is the importance of LLC14 ◾ MACHINE LEARNING.

Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLC14 ◾ Machine Learning yet-unseen labeled data will be used to test them, they will fail miserably.

Define A.

Testing data A training set is used to build the model and testing set is used to validate the built model.

What do you mean by LARGER.

Larger portion of the data is used for model training purpose, and the test metrics of the model are tested on holdout data.

Explain in detail TECHNIQUE OF CROSS-VALIDATION.

The technique of cross-validation is useful when the available training dataset is quite small and one cannot afford to hold out part of the data just for validation purposes.

What is the importance of TRAINING OF THE MODEL.

Each of these k folds are treated as holdout datasets, and the training of the model is performed on rest of the k − 1 folds.

Define MACHINE LEARNING ALGORITHMS MACHINE.

1.3 Applications of Machine Learning Algorithms Machine learning has proven itself to be the answer to many real-world challenges, but there are still a number of prob-lems for which machine learning breakthrough is required.

What do you mean by EX-CHAIRMAN OF MICROSOFT.

The need was felt by the cofounder and ex-chairman of Microsoft, Bill Gates, and was translated into the following wordings on one occasion [6]: A breakthrough in machine learning would be worth ten Microsofts Click here to order "Machine Learning: Algorithms and Applications" International Standard Book Number-13: 978-1-4987-0538-7 (Hardback) © 2017 by Taylor & Francis Group, LLCIntroduction to Machine Learning ◾ 15 In this section, we will discuss some applications of machine learning with some examples.

What do you mean by RECOGNITION OF HANDWRITTEN POSTAL CODES TODAY.

1.3.1 Automatic Recognition of Handwritten Postal Codes Today, in order to communicate, we use a variety of digital devices.