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, Data Pipelines, 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, covers key clustering methods such as K-means clustering, Gaussian mixture Expectation-Maximization and function approximation.

Chapter 5, Dimension Reduction, describes the Kullback-Leibler divergence, the principal component analysis for linear models followed by an overview of manifold applied to non-linear models.

Chapter 6, Naive Bayes Classifiers, focuses on the probabilistic graphical models and more specifically the implementation of Naive Bayes models and its application to text mining.

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, Monte Carlo Inference, describes Gaussian sampling using Box-Muller technique, Bootstrap replication with replacement, and the ubiquitous Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

Chapter 9, 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 10, Multilayer Perception, describes feed-forward neural networks followed by a full implementation of the multilayer perceptron classifier.

Chapter 11, Deep Learning, implements a sparse auto encoder and a restricted Boltzmann machines for dimension reduction in Scala followed by an overview of the convolutional neural network.

Chapter 12, 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 13, Evolutionary Computing, covers describes the basics of evolutionary computing and the implementation of the different components of a multipurpose genetic algorithm.

Chapter 14, Multiarmed Bandits, Multiarmed Bandits, introduces the concept of exploration-exploitation trade-off using Epsilon-greedy algorithm, the Upper confidence bound technique and the context-free Thompson sampling.

Chapter 15, Reinforcement Learning, covers 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 16, Parallelism in Scala and Akka, describes some of the artifacts and frameworks to create scalable applications and evaluate the relative performance of Scala parallel collections and Akka-based distributed computation.

Chapter 17, Apache Spark MLlib, covers the architecture and key concepts of Apache Spark, machine learning leveraging resilient distributed datasets, reusable ML pipelines, extending MLlib with distributed divergences and an example of Spark streaming library.

Appendix A, Basic Concepts, describes the Scala language constructs used throughout the book, elements of linear algebra and optimization techniques.

Appendix B, References, provides a chapter-wise list of references [source, entry] for each chapter.