This textbook covers the broader field of artificial intelligence.
The chapters for this textbook span within three categories: Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions.
The underlying methods include search and logic-based methods.
These methods are discussed in Chapters 1through 5.
Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses.
Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models.
These methods are discussed in Chapters 6 through 11.
Integrating Reasoning and Learning: Chapters 11 and 12 discuss techniques for integrating reasoning and learning.
Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.
The primary audience for this textbook are professors and advanced-level students in computer science.
It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course.
Professionals working in this related field many also find this textbook useful as a reference.
About author(s): Charu C.
Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.
Watson Research Center in Yorktown Heights, New York.
He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.
from the Massachusetts Institute of Technology in 1996.
He has worked extensively in the field of data mining.
He has published more than 400 papers in refereed conferences and journals and authored over 80 patents.
He is the author or editor of 19 books, including textbooks on data mining, recommender systems, and outlier analysis.
Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM.
He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of two IBM Outstanding Technical Achievement Awards (2009, 2015) for his work on data streams/high-dimensional data.
He received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining.
He is also a recipient of the IEEE ICDM Research Contributions Award (2015) and the ACM SIGKDD Innovations Award (2019), which are the two highest awards for influential research contributions in data mining.
He has served as the general co-chair of the IEEE Big Data Conference (2014) and as the program co-chair of the ACM CIKM Conference (2015), the IEEE ICDM Conference (2015), and the ACM KDD Conference (2016).
He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008.
He is an associate editor of the IEEE Transactions on Big Data, an action editor of the Data Mining and Knowledge Discovery Journal, and an associate editor of the Knowledge and Information Systems Journal.
He serves as the editor-in-chief of the ACM Transactions on Knowledge Discovery from Data as well as the ACM SIGKDD Explorations.
He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer.
He has served as the vice-president of the SIAM Activity Group on Data Mining and is a member of the SIAM industry committee.
He is a fellow of the SIAM, ACM, and the IEEE, for contributions to knowledge discovery and data mining algorithms.
Categories | Deductive reasoning methodsthese methods start with predefined hypotheses and reason with them in order to arrive at logically sound conclusions |
---|---|
Methods | These methods start with examples and use statistical methods in order to arrive at hypotheses |
Learning | Chapters 11 and 12 discuss techniques for integrating reasoning and learning |
About author(s) | Charu |
Springer este o companie de editură proeminentă la nivel mondial, specializată în literatura academică și științifică.
Fondată în 1842 la Berlin, Germania, Springer a crescut pentru a deveni unul dintre cei mai mari și mai respectați editori din lume, cu birouri și operațiuni în numeroase țări.
Springer publică o gamă largă de reviste academice, cărți, lucrări de referință și baze de date online care acoperă o gamă largă de discipline, inclusiv știință, tehnologie, medicină, inginerie, matematică, umaniste, științe sociale și afaceri.
Catalogul extins al companiei include: 1.
Reviste: Springer publică mii de reviste academice evaluate de colegi care acoperă un spectru larg de discipline.
Aceste reviste prezintă articole de cercetare originale, recenzii și contribuții academice din partea experților în domeniile lor respective.
Cărți: Springer publică o selecție diversă de cărți, inclusiv manuale, monografii, lucrări de referință și titluri profesionale.
Aceste cărți acoperă o gamă largă de subiecte și se adresează cercetătorilor, studenților, profesioniștilor și practicienilor.
Lucrări de referință: Springer produce lucrări de referință cu autoritate, cum ar fi enciclopedii, manuale, dicționare și atlase, care oferă o acoperire cuprinzătoare a unor subiecte și discipline specifice.
Baze de date online: Springer oferă baze de date și platforme online care oferă acces la vasta sa colecție de conținut academic.
Aceste platforme permit utilizatorilor să caute, să răsfoiască și să acceseze literatură academică, reviste, cărți și materiale de referință.
Springer este cunoscut pentru angajamentul său față de calitate, integritate și inovație în publicarea academică.
Compania lucrează îndeaproape cu autori, editori, recenzori și instituții academice pentru a asigura cele mai înalte standarde de excelență și rigoare academică în publicațiile sale.
Prin urmare, Springer este considerată pe scară largă ca o sursă de încredere de informații academice și o resursă valoroasă pentru cercetători, studenți și profesioniști din întreaga lume.