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Ameet Joshi Machine Learning and Artifi cial Intelligence 2nd Edition Machine Learning and Artificial Intelligence Ameet V. Joshi Machine Learning and Artificial Intelligence Second Edition AmeetV.Joshi Microsoft(UnitedStates) Redmond,WA,USA ISBN978-3-031-12281-1 ISBN978-3-031-12282-8 (eBook) https://doi.org/10.1007/978-3-031-12282-8 1stedition:©SpringerNatureSwitzerlandAG2020 2ndedition:©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2023 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Toeveryonewhobelievesinhuman intelligencetobematureenoughtocoexist withmachineintelligenceandbebenefitted andentertainedbyit... Preface to the Second Edition OneofthegreatestphysicistsofalltimeandNobelLaureateDr.RichardFeynman was once asked by his peer to explain a property of Fermi Dirac statistics, which wasthenaveryrecentdiscovery.Feynmanquicklysaid, NotonlyIwillexplainittoyou,butIwillpreparealectureonitforfreshmanlevel. However,quiteunusually,afterfewdays,hecamebackandadmitted, Icouldnotdoit.Ijustcouldnotreducetheexplanationtofreshmanlevel.Thatmeanswe reallydon’tunderstandit. It was quite a bold remark coming from even Dr. Feynman. However, apart from thetopicofFermiDiracstatisticsitself,italludestoaverydeepthoughtaboutour understandingofthingsingeneral.Freshmanlevelhereessentiallymeantsomething thatcanbederiveddirectlyusingthefirstprinciplesinmathematicsorphysics.This thought has always made me conscious to try and explain everything that I claim to understand using firstprinciples, try to explain everything conceptually and not onlyusingelaboratesetofequations. The area of artificial intelligence and machine learning has exploded over last decade. With the widespread popularity, the core concepts in the field have been sometimes diluted and sometimes reinterpreted. With such exponential growth in the area, the scope of the field has also grown in proportion. A newcomer in the area can quickly find the topic daunting and confusing. One can always start with searching the relevant topics on the Web, or just start with Wikipedia, but more oftenthannoteverysingletopicopensarabbit-holewithmoreandmorenewand unknown concepts, and one can get lost very easily. Also, most of the concepts inmachinelearningaredeeplyrootedinmathematicsandstatistics.Withoutsolid background in theoretical mathematics and statistics, the sophisticated derivations of the theorems and lemmas can make one feel confused and disinterested in the area. I have made an attempt here to introduce most fundamental topics in machine learning and their applications to build artificially intelligent solutions with an intuitive and conceptual approach. There would be some mathematical guidance vii viii PrefacetotheSecondEdition usedfromtimetotime,withoutwhichtheconceptswouldnotbesufficientlyclear, butIhavetriedtoavoidcomplexderivationsandproofstomakethecontentmore accessible to readers that are not from strong mathematical background. In the process,asperDr.Feynman,alsomakingsureIhaveunderstoodthemmyself.As farasgeneralmathematicalandstatisticalrequirementsgo,Iwouldsaythattypical undergraduate level should suffice. Also, with proliferation and standardization of the machine learning libraries in open source domain, one does not need to go that deep into mathematical understanding of the theory to be able to implement the state-of-the-art machine learning models leading to state-of-the-art intelligent solutions. Oneofthemainsourcesofconfusionthatariseswhentryingtosolveaproblem in the given application is the choice of algorithm. Typically each algorithm presented here has originated from some specific problem, but the algorithm is typically not restricted to solving only that problem. However, choosing the right algorithm for given problem is not trivial even for a doctoral fellow with strong mathematicalbackground.Inordertoseparatethesetwoareas,Ihavedividedthese two areas into separate parts altogether. This will make the topics much easier to accessforthereader. IwouldrecommendthereadertostartwithPartI,andthenchoosePartsIIorIII dependingontheneeds.Itwillbeidealforastudenttogosequentiallythroughthe book,whileanewcomertotheareafromprofessionalbackgroundwouldbebetter suitedtostartwithPartIIItounderstandorfocusonthepreciseapplicationathand andthendelveintothedetailsofthetheoryforthealgorithmsasneededinPartII. PartsIVandVshouldfollowafterwards.Ihaveaddedsufficientreferencesbetween thetwopartstomakethistransitionsmooth. In my mind, unless one can see the models in action on real data that one can see and plot, the understanding is not complete. Hence, following the details of algorithms and applications, I have added another part to cover the basic implementation of the models using free and open source options. Completion of thispartwillenablethereadertotacklethereal-worldproblemsinAIwithstate-of- the-artMLtechniques! Redmond,WA,USA AmeetV.Joshi March2019 Preface to the First Edition ThefirsteditionofthebookreceivedaresoundingresponsefromthereadersandI wasextremelythrilledtohavetheopportunitytohaveafollowupsecondeditionof thebook. Ihavebeencollectingfeedbackfrommultiplereadersandreviewersoverthelast couple of years and also keeping close with the new developments happening the field. There was certainly more content that was ready to be added to the book, as well as some content needed edits. Also, this time Springer suggested that I should be creating a textbook version rather than reference book version as was thecasewithfirstedition.Thismeantmakingthecontentmoretunedforcollegiate curriculum.ItwasachallengethatIwashappytoaccept. So,thissecondeditionofthebookisprimarilytargetedtoserveasatextbookfor anundergradorgraduatelevelcourseonmachinelearningandartificialintelligence. The book is now organized into five parts including a conclusion. The core of the bookborrowstheconceptsfromfirsteditionbutthenandaddsimplementationsto all the algorithms as they are explained in the same chapter. This way, students can now complete the understanding of the concepts with implementation and applicationtorealworldproblems. Part I of the book sets the stage for learning the vast array of machine learning techniquesandtheirapplicationswithfoundationaryconcepts,andimplementation platformdetails. Part II of the book dives deep into the theory of machine learning techniques coupledwithimplementingthemusingcloudbasedopensourceresources. Part III Integrates all these concepts to study how to build end to end machine learningpipelinesandperformancemeasurementoftheimplementedMLmodels. Part IV focuses on artificial intelligence, which is essentially application of all thetechniqueslearnedsofartocreateintelligentexperiencesinpracticalsituations. ix x PrefacetotheFirstEdition I wish good luck to all the student embarking on the awesome journey to learn a true twentieth century science of machine learning and its application to create artificialintelligence.Hopethatthisbookwillgivethemasolidfoundationtobuild theirfuturecareers. Redmond,WA,USA AmeetV.Joshi March2022 Acknowledgments Iwouldliketousethisopportunitytoacknowledgethepeoplewhomadesignificant contributions towards creation of the second edition of this book. It was certainly a daunting task to update on the first edition of the book that already was a huge success. However,continuous supportandencouragement frommywife,Meghana, and sons,DhroovandSushaan,reallyhelpedmecontinuewiththeeffortsandultimately completethebook.IwouldalsoliketothankmymotherMadhuri,fatherVijay,and brotherMandarfortheircontinuousencouragementandsupport. IwouldliketothankMaryJamesofSpringerfortheencouragementandsupport asIworkedthroughthecompletionofthebook.IwouldalsoliketothankAmrita, Zoe,Brian,andthewholeteamofSpringerfortheirtimelyhelpwithupdates. Thepresentbookisafurtheranceoftheeffortsfromthepublicationofthefirst edition towards unification of the disparate areas in the field of machine learning to produce artificially intelligent experiences. The book essentially stands on the pillarsofthisknowledgethatwascreatedbythenumerousextraordinaryscientists and brilliant mathematicians over several decades. So, I would like to thank them allaswell. Last but not the least, I would like to thank all the readers who provided me with valuable feedback and thousands of readers who used this book towards understanding the concepts in this area. Their critic and encouragement certainly helpedmeimproveonthefirsteditionofthisbook. xi