Book Image

Scala for Machine Learning, Second Edition - Second Edition

Book Image

Scala for Machine Learning, Second Edition - Second Edition

Overview of this book

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You’ll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.

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Table of Contents (27 chapters)
Scala for Machine Learning Second Edition
Scala for Machine Learning Second Edition
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Getting Started
Getting Started
Mathematical notations for the curious
Why machine learning?
Why Scala?
Model categorization
Taxonomy of machine learning algorithms
Leveraging Java libraries
Tools and frameworks
Source code
Let's kick the tires
Summary
2
Data Pipelines
Data Pipelines
Modeling
Defining a methodology
Monadic data transformation
Workflow computational model
Profiling data
Assessing a model
Summary
3
Data Preprocessing
Data Preprocessing
Time series in Scala
Moving averages
Fourier analysis
The discrete Kalman filter
Alternative preprocessing techniques
Summary
4
Unsupervised Learning
Unsupervised Learning
K-mean clustering
Expectation-Maximization (EM)
Summary
5
Dimension Reduction
Dimension Reduction
Challenging model complexity
The divergences
Principal components analysis (PCA)
Nonlinear models
Summary
6
Naïve Bayes Classifiers
Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Summary
7
Sequential Data Models
Sequential Data Models
Markov decision processes
The hidden Markov model (HMM)
Conditional random fields
Regularized CRF and text analytics
Comparing CRF and HMM
Performance consideration
Summary
8
Monte Carlo Inference
Monte Carlo Inference
The purpose of sampling
Gaussian sampling
Monte Carlo approximation
Bootstrapping with replacement
Markov Chain Monte Carlo (MCMC)
Summary
9
Regression and Regularization
Regression and Regularization
Linear regression
Regularization
Numerical optimization
Logistic regression
Summary
10
Multilayer Perceptron
Multilayer Perceptron
Feed-forward neural networks (FFNN)
The multilayer perceptron (MLP)
Evaluation
Benefits and limitations
Summary
11
Deep Learning
Deep Learning
Sparse autoencoder
Restricted Boltzmann Machines (RBMs)
Convolution neural networks
12
Kernel Models and SVM
Kernel Models and SVM
Kernel functions
The support vector machine (SVM)
Performance considerations
Summary
13
Evolutionary Computing
Evolutionary Computing
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Summary
14
Multiarmed Bandits
Multiarmed Bandits
K-armed bandit
Thompson sampling
Upper bound confidence
Summary
15
Reinforcement Learning
Reinforcement Learning
Reinforcement learning
Learning classifier systems
Summary
16
Parallelism in Scala and Akka
Parallelism in Scala and Akka
Overview
Scala
Scalability with Actors
Akka
Summary
17
Apache Spark MLlib
Apache Spark MLlib
Overview
Apache Spark core
MLlib library
Reusable ML pipelines
Extending Spark
Streaming engine
Performance evaluation
Pros and cons
Summary
Basic Concepts
Basic Concepts
Scala programming
Mathematics
Finances 101
Suggested online courses
References
References
References
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Index
Index

Chapter 7

[7:1] A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, L. Rabiner - Proceedings of the IEEE Volume 77, Feb 1989 - http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf

[7:2] CRF Java library v 1.3 S. Sarawagi - Indian Institute of Technology, Bombay 2008 - http://crf.sourceforge.net/

[7:3] Introduction to Machine Learning §13.2 Discrete Markov Processes E. Alpaydin - The MIT Press 2004

[7:4] A Revealing Introduction to Hidden Markov Models M. Stamp - Dept. of Computer Science, San Jose State University 2012 - http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf

[7:5] A brief introduction to Dynamic Programming A. Kasibhatla - Nanocad Lab University of California, Los Angeles 2010 -http://nanocad.ee.ucla.edu/pub/Main/SnippetTutorial/Amar_DP_Intro.pdf

[7:6] Pattern Recognition and Machine Learning §13.2.1 Maximum Likelihood for the HMM C. Bishop –Springer 2006

[7:7] Dynamic Programing in Machine learning – Part 3: Viterbi Algorithm and Machine Learning E. Nichols - Nara Institute of Science and Technology - https://www.youtube.com

[7:8] American Association of Individual Investors (AAII) - http://www.aaii.com

[7:9] Conditional Random Fields: Probabilistic Models for Segmenting and Labelling Sequence Data – J. Lafferty - Carnegie Mellon University A. McCallum, University of Massachusetts F. Pereira - University of Pennsylvania 2001

[7:10] Machine Learning for Multimedia Content Analysis §9.6 Conditional Random Fields case study Y. Gong, W. Xu - Springer 2007

[7:11] Machine Learning: A Probabilistic Perspective §19.6.2.4 Conditional random fields Natural language parsing K Murphy - MIT Press 2012

[7:12] Conditional Random Field §3 Various Interfaces KReSIT - IIT Bombay 2004 - http://crf.sourceforge.net/introduction/interfaces.html#FeatureGenerator

[7:13] Distributed Training for Conditional Random Fields X. Lin, L Zhao, D Yu, X. Wu Key - Laboratory of Machine Perception and Intelligence School of Electronics Engineering and computer science China 2010 - http://www.klmp.pku.edu.cn/Paper/UsrFile/97.pdf

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