000 04021nam a22005895i 4500
001 978-3-030-26622-6
003 DE-He213
005 20220801213740.0
007 cr nn 008mamaa
008 190924s2020 sz | s |||| 0|eng d
020 _a9783030266226
_9978-3-030-26622-6
024 7 _a10.1007/978-3-030-26622-6
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
100 1 _aJoshi, Ameet V.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_934087
245 1 0 _aMachine Learning and Artificial Intelligence
_h[electronic resource] /
_cby Ameet V Joshi.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXXII, 261 p. 98 illus., 94 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Part I Introduction to AI and ML -- Essential concepts in AL and ML -- Part II Techniques for Static Machine Learning Models -- Perceptron and Neural Networks -- Decision Trees -- Advanced Decision Trees -- Support Vector Machines -- Probabilistic Models -- Deep Learning -- Part III Techniques for Dynamic Machine Learning Models -- Autoregressive and Moving Average Models -- Hidden Markov Models and Conditional Random Fields -- Recurrent Neural Networks -- Part IV Applications -- Classification Regression -- Ranking -- Clustering -- Recommendations -- Next Best Actions -- Designing ML Pipelines -- Using ML Libraries -- Azure Machine Learning Studio -- Conclusions.
520 _aThis book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.
650 0 _aTelecommunication.
_910437
650 0 _aMachine learning.
_91831
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 0 _aInformation storage and retrieval systems.
_922213
650 0 _aQuantitative research.
_94633
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_934088
650 2 4 _aInformation Storage and Retrieval.
_923927
650 2 4 _aData Analysis and Big Data.
_934089
710 2 _aSpringerLink (Online service)
_934090
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030266219
776 0 8 _iPrinted edition:
_z9783030266233
776 0 8 _iPrinted edition:
_z9783030266240
856 4 0 _uhttps://doi.org/10.1007/978-3-030-26622-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c75551
_d75551