What this book covers
Chapter 1, Getting Started, introduces the basic concepts of statistical analysis, classification, regression, prediction, clustering, and optimization. This chapter covers the Scala languages features and libraries, followed by the implementation of a simple application.
Chapter 2, Hello World!, describes a typical workflow for classification, the concept of bias/variance trade-off, and validation using the Scala dependency injection applied to the technical analysis of financial markets.
Chapter 3, Data Preprocessing, covers time series analyses and leverages Scala to implement data preprocessing and smoothing techniques such as moving averages, discrete Fourier transform, and the Kalman recursive filter.
Chapter 4, Unsupervised Learning, focuses on the implementation of some of the most widely used clustering techniques, such as K-means, the expectation-maximization, and the principal component analysis as a dimension reduction method.
Chapter 5, Naïve Bayes Classifiers, introduces probabilistic graphical models, and then describes the implementation of the Naïve Bayes and the multivariate Bernoulli classifiers in the context of text mining.
Chapter 6, Regression and Regularization, covers a typical implementation of the linear and least squares regression, the ridge regression as a regularization technique, and finally, the logistic regression.
Chapter 7, Sequential Data Models, introduces the Markov processes followed by a full implementation of the hidden Markov model, and conditional random fields applied to pattern recognition in financial market data.
Chapter 8, Kernel Models and Support Vector Machines, covers the concept of kernel functions with implementation of support vector machine classification and regression, followed by the application of the one-class SVM to anomaly detection.
Chapter 9, Artificial Neural Networks, describes feed-forward neural networks followed by a full implementation of the multilayer perceptron classifier.
Chapter 10, Genetic Algorithms, covers the basics of evolutionary computing and the implementation of the different components of a multipurpose genetic algorithm.
Chapter 11, Reinforcement Learning, introduces the concept of reinforcement learning with an implementation of the Q-learning algorithm followed by a template to build a learning classifier system.
Chapter 12, Scalable Frameworks, covers some of the artifacts and frameworks to create scalable applications for machine learning such as Scala parallel collections, Akka, and the Apache Spark framework.
Appendix A, Basic Concepts, covers the Scala constructs used throughout the book, elements of linear algebra, and an introduction to investment and trading strategies.
Appendix B, References, provides a chapter-wise list of references for [source entry] in the respective chapters. This appendix is available as an online chapter at https://www.packtpub.com/sites/default/files/downloads/8742OS_AppendixB_References.pdf.
Short test applications using financial data illustrate the large variety of predictive, regression, and classification models.
The interdependencies between chapters are kept to a minimum. You can easily delve into any chapter once you complete Chapter 1, Getting Started, and Chapter 2, Hello World!.
