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Scala for Machine Learning, Second Edition - Second Edition

You're reading from  Scala for Machine Learning, Second Edition - Second Edition

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Pages 740 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (27) Chapters close

Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib Basic Concepts References Index

Chapter 15


[15:1] Reinforcement Learning: An introduction R.S. Sutton, A. Barto - MIT Press 1998

[15:2] Reinforcement Learning and Plan Recognition M. Veloso - Computer Science Dept. Carnegie Mellon University 2001 - http://www.cs.cmu.edu/~reids/planning/handouts/RL-HMM.pdf

[15:3] Reinforcement learning: A Brief Tutorial D. Precup - Reasoning and Learning Lab, McGill University 2005 - http://www.iro.umontreal.ca/~lisa/seminaires/14-09-2005.pdf

[15:4] Programming in Scala 2nd Edition §18 Stateful Objects M. Odersky, L. Spoon, B. Venners - Artima 2008

[15:5] Scala for the Impatient §15.6 Annotations for Optimizations C. Horstmann - Addison-Wesley 2012

[15:6] Reinforcement Learning for automatic financial trading: Introduction and some applications F. Bertoluzzo, M. Corazza, Ca'Foscari - University of Venice 2012 - http://www.unive.it/media/allegato/DIP/Economia/Working_papers/Working_papers_2012/WP_DSE_bertoluzzo_corazza_33_12.pdf

[15:7] The Options Institute tutorial - Chicago Board of Options Exchange - http://www.cboe.com/LearnCenter/Tutorials.aspx#basics

[15:8] Black-Scholes model Wikipedia the Free encyclopedia Wikimedia Foundation - http://en.wikipedia.org/wiki/Black-Scholes_model

[15:9] Value Function Approximation in Reinforcement Learning using the Fourier Basis G. Konidaris, S. Osentoski, P. Thomas - http://lis.csail.mit.edu/pubs/konidaris-aaai11a.pdf

[15:10] A mathematical framework for studying learning in classifier systems. J. Holland Physica D, volume 2, §1-3 1986

[15:11] Introduction to Learning Classifier Systems Tutorial J. Bacardit, N. Krasnogor - G53 Bioinformatics University of Nottingham - http://www.exa.unicen.edu.ar/escuelapav/cursos/bio/l7.pdf

[15:12] Learning Classifier Systems: A Gentle Introduction: P. L.Lanzi - Politecnico Di Milano GECCO 2014 - http://www.slideshare.net/pierluca.lanzi/gecco2014-learning-classifier-systems-a-gentle-introduction

[15:13] Automated Stock Trading and Portfolio Optimization Using XCS Trader and Technical Analysis A. Chauban - Schools of Informatics, University of Edinburgh 2008 - http://www.inf.ed.ac.uk/publications/thesis/online/IM080575.pdf

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