Scala for Machine Learning -- Table of Contents -- Scala for Machine Learning -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why subscribe? -- Free access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 1. Getting Started -- Mathematical notation for the curious -- Why machine learning? -- Classification -- Prediction -- Optimization -- Regression -- Why Scala? -- Abstraction -- Scalability -- Configurability -- Maintainability -- Computation on demand -- Model categorization -- Taxonomy of machine learning algorithms -- Unsupervised learning -- Clustering -- Dimension reduction -- Supervised learning -- Generative models -- Discriminative models -- Reinforcement learning -- Tools and frameworks -- Java -- Scala -- Apache Commons Math -- Description -- Licensing -- Installation -- JFreeChart -- Description -- Licensing -- Installation -- Other libraries and frameworks -- Source code -- Context versus view bounds -- Presentation -- Primitives and implicits -- Primitive types -- Type conversions -- Operators -- Immutability -- Performance of Scala iterators -- Let's kick the tires -- Overview of computational workflows -- Writing a simple workflow -- Selecting a dataset -- Loading the dataset -- Preprocessing the dataset -- Basic statistics -- Normalization and Gauss distribution -- Plotting data -- Creating a model (learning) -- Classify the data -- Summary -- 2. Hello World! -- Modeling -- A model by any other name -- Model versus design -- Selecting a model's features -- Extracting features -- Designing a workflow -- The computational framework -- The pipe operator.

Monadic data transformation -- Dependency injection -- Workflow modules -- The workflow factory -- Examples of workflow components -- The preprocessing module -- The clustering module -- Assessing a model -- Validation -- Key metrics -- Implementation -- K-fold cross-validation -- Bias-variance decomposition -- Overfitting -- Summary -- 3. Data Preprocessing -- Time series -- Moving averages -- The simple moving average -- The weighted moving average -- The exponential moving average -- Fourier analysis -- Discrete Fourier transform (DFT) -- DFT-based filtering -- Detection of market cycles -- The Kalman filter -- The state space estimation -- The transition equation -- The measurement equation -- The recursive algorithm -- Prediction -- Correction -- Kalman smoothing -- Experimentation -- Alternative preprocessing techniques -- Summary -- 4. Unsupervised Learning -- Clustering -- K-means clustering -- Measuring similarity -- Overview of the K-means algorithm -- Step 1 - cluster configuration -- Defining clusters -- Defining K-means -- Initializing clusters -- Step 2 - cluster assignment -- Step 3 - iterative reconstruction -- Curse of dimensionality -- Experiment -- Tuning the number of clusters -- Validation -- Expectation-maximization (EM) algorithm -- Gaussian mixture model -- EM overview -- Implementation -- Testing -- Online EM -- Dimension reduction -- Principal components analysis (PCA) -- Algorithm -- Implementation -- Test case -- Evaluation -- Other dimension reduction techniques -- Performance considerations -- K-means -- EM -- PCA -- Summary -- 5. Naïve Bayes Classifiers -- Probabilistic graphical models -- Naïve Bayes classifiers -- Introducing the multinomial Naïve Bayes -- Formalism -- The frequentist perspective -- The predictive model -- The zero-frequency problem -- Implementation -- Software design -- Training -- Classification.

Labeling -- Results -- Multivariate Bernoulli classification -- Model -- Implementation -- Naïve Bayes and text mining -- Basics of information retrieval -- Implementation -- Extraction of terms -- Scoring of terms -- Testing -- Retrieving textual information -- Evaluation -- Pros and cons -- Summary -- 6. Regression and Regularization -- Linear regression -- One-variate linear regression -- Implementation -- Test case -- Ordinary least squares (OLS) regression -- Design -- Implementation -- Test case 1 - trending -- Test case 2 - features selection -- Regularization -- Ln roughness penalty -- The ridge regression -- Implementation -- The test case -- Numerical optimization -- The logistic regression -- The logit function -- Binomial classification -- Software design -- The training workflow -- Configuring the least squares optimizer -- Computing the Jacobian matrix -- Defining the exit conditions -- Defining the least squares problem -- Minimizing the loss function -- Test -- Classification -- Summary -- 7. Sequential Data Models -- Markov decision processes -- The Markov property -- The first-order discrete Markov chain -- The hidden Markov model (HMM) -- Notation -- The lambda model -- HMM execution state -- Evaluation (CF-1) -- Alpha class (the forward variable) -- Beta class (the backward variable) -- Training (CF-2) -- Baum-Welch estimator (EM) -- Decoding (CF-3) -- The Viterbi algorithm -- Putting it all together -- Test case -- The hidden Markov model for time series analysis -- Conditional random fields -- Introduction to CRF -- Linear chain CRF -- CRF and text analytics -- The feature functions model -- Software design -- Implementation -- Building the training set -- Generating tags -- Extracting data sequences -- CRF control parameters -- Putting it all together -- Tests -- The training convergence profile.

Impact of the size of the training set -- Impact of the L2 regularization factor -- Comparing CRF and HMM -- Performance consideration -- Summary -- 8. Kernel Models and Support Vector Machines -- Kernel functions -- Overview -- Common discriminative kernels -- The support vector machine (SVM) -- The linear SVM -- The separable case (hard margin) -- The nonseparable case (soft margin) -- The nonlinear SVM -- Max-margin classification -- The kernel trick -- Support vector classifier (SVC) -- The binary SVC -- LIBSVM -- Software design -- Configuration parameters -- SVM Formulation -- The SVM kernel function -- SVM execution -- SVM implementation -- C-penalty and margin -- Kernel evaluation -- Application to risk analysis -- Features and labels -- Anomaly detection with one-class SVC -- Support vector regression (SVR) -- Overview -- SVR versus linear regression -- Performance considerations -- Summary -- 9. Artificial Neural Networks -- Feed-forward neural networks (FFNN) -- The Biological background -- The mathematical background -- The multilayer perceptron (MLP) -- The activation function -- The network architecture -- Software design -- Model definition -- Layers -- Synapses -- Connections -- Training cycle/epoch -- Step 1 - input forward propagation -- The computational model -- Objective -- Softmax -- Step 2 - sum of squared errors -- Step 3 - error backpropagation -- Error propagation -- The computational model -- Step 4 - synapse/weights adjustment -- Momentum factor for gradient descent -- Implementation -- Step 5 - convergence criteria -- Configuration -- Putting all together -- Training strategies and classification -- Online versus batch training -- Regularization -- Model instantiation -- Prediction -- Evaluation -- Impact of learning rate -- Impact of the momentum factor -- Test case -- Implementation -- Models evaluation.

Impact of hidden layers architecture -- Benefits and limitations -- Summary -- 10. Genetic Algorithms -- Evolution -- The origin -- NP problems -- Evolutionary computing -- Genetic algorithms and machine learning -- Genetic algorithm components -- Encodings -- Value encoding -- Predicate encoding -- Solution encoding -- The encoding scheme -- Flat encoding -- Hierarchical encoding -- Genetic operators -- Selection -- Crossover -- Mutation -- Fitness score -- Implementation -- Software design -- Key components -- Selection -- Controlling population growth -- GA configuration -- Crossover -- Population -- Chromosomes -- Genes -- Mutation -- Population -- Chromosomes -- Genes -- The reproduction cycle -- GA for trading strategies -- Definition of trading strategies -- Trading operators -- The cost/unfitness function -- Trading signals -- Trading strategies -- Signal encoding -- Test case -- Data extraction -- Initial population -- Configuration -- GA instantiation -- GA execution -- Tests -- The unweighted score -- The weighted score -- Advantages and risks of genetic algorithms -- Summary -- 11. Reinforcement Learning -- Introduction -- The problem -- A solution - Q-learning -- Terminology -- Concept -- Value of policy -- Bellman optimality equations -- Temporal difference for model-free learning -- Action-value iterative update -- Implementation -- Software design -- States and actions -- Search space -- Policy and action-value -- The Q-learning training -- Tail recursion to the rescue -- Prediction -- Option trading using Q-learning -- Option property -- Option model -- Function approximation -- Constrained state-transition -- Putting it all together -- Evaluation -- Pros and cons of reinforcement learning -- Learning classifier systems -- Introduction to LCS -- Why LCS -- Terminology -- Extended learning classifier systems (XCS) -- XCS components.

Application to portfolio management.