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Machine learning in finance : from theory to practice

Machine learning in finance : from theory to practice (Loan 1 times)

Material type
단행본
Personal Author
Dixon, Matthew F. Halperin, Igor. Bilokon, Paul.
Title Statement
Machine learning in finance : from theory to practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon.
Publication, Distribution, etc
Cham :   Springer,   c2020.  
Physical Medium
xxv, 548 p. : ill. (some col.) ; 25 cm.
ISBN
9783030410674 9783030410681 (ebk.)
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Financial engineering. Finance --Mathematical models.
000 00000cam u2200205 a 4500
001 000046086939
005 20210722100847
008 210721s2020 sz a b 001 0 eng d
020 ▼a 9783030410674
020 ▼a 9783030410681 (ebk.)
035 ▼a (KERIS)BIB000015637143
040 ▼a 223009 ▼d 223009 ▼c 211009 ▼d 211009
082 0 4 ▼a 332.0285631 ▼2 23
084 ▼a 332.0285631 ▼2 DDCK
090 ▼a 332.0285631 ▼b D621m
100 1 ▼a Dixon, Matthew F.
245 1 0 ▼a Machine learning in finance : ▼b from theory to practice / ▼c Matthew F. Dixon, Igor Halperin, Paul Bilokon.
260 ▼a Cham : ▼b Springer, ▼c c2020.
300 ▼a xxv, 548 p. : ▼b ill. (some col.) ; ▼c 25 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Financial engineering.
650 0 ▼a Finance ▼x Mathematical models.
700 1 ▼a Halperin, Igor.
700 1 ▼a Bilokon, Paul.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 332.0285631 D621m Accession No. 121257732 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Chapter 1. Introduction.- Chapter 2. Probabilistic Modeling.- Chapter 3. Bayesian Regression & Gaussian Processes.- Chapter 4. Feed Forward Neural Networks.- Chapter 5. Interpretability.- Chapter 6. Sequence Modeling.- Chapter 7. Probabilistic Sequence Modeling.- Chapter 8. Advanced Neural Networks.- Chapter 9. Introduction to Reinforcement learning.- Chapter 10. Applications of Reinforcement Learning.- Chapter 11. Inverse Reinforcement Learning and Imitation Learning.- Chapter 12. Frontiers of Machine Learning and Finance.

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