Clojure for Machine Learning -- Table of Contents -- Clojure 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 -- Downloading the color images of this book -- Errata -- Piracy -- Questions -- 1. Working with Matrices -- Introducing Leiningen -- Representing matrices -- Generating matrices -- Adding matrices -- Multiplying matrices -- Transposing and inverting matrices -- Interpolating using matrices -- Summary -- 2. Understanding Linear Regression -- Understanding single-variable linear regression -- Understanding gradient descent -- Understanding multivariable linear regression -- Gradient descent with multiple variables -- Understanding Ordinary Least Squares -- Using linear regression for prediction -- Understanding regularization -- Summary -- 3. Categorizing Data -- Understanding the binary and multiclass classification -- Understanding the Bayesian classification -- Using the k-nearest neighbors algorithm -- Using decision trees -- Summary -- 4. Building Neural Networks -- Understanding nonlinear regression -- Representing neural networks -- Understanding multilayer perceptron ANNs -- Understanding the backpropagation algorithm -- Understanding recurrent neural networks -- Building SOMs -- Summary -- 5. Selecting and Evaluating Data -- Understanding underfitting and overfitting -- Evaluating a model -- Understanding feature selection -- Varying the regularization parameter -- Understanding learning curves -- Improving a model -- Using cross-validation -- Building a spam classifier -- Summary.

6. Building Support Vector Machines -- Understanding large margin classification -- Alternative forms of SVMs -- Linear classification using SVMs -- Using kernel SVMs -- Sequential minimal optimization -- Using kernel functions -- Summary -- 7. Clustering Data -- Using K-means clustering -- Clustering data using clj-ml -- Using hierarchical clustering -- Using Expectation-Maximization -- Using SOMs -- Reducing dimensions in the data -- Summary -- 8. Anomaly Detection and Recommendation -- Detecting anomalies -- Building recommendation systems -- Content-based filtering -- Collaborative filtering -- Using the Slope One algorithm -- Summary -- 9. Large-scale Machine Learning -- Using MapReduce -- Querying and storing datasets -- Machine learning in the cloud -- Summary -- A. References -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- Chapter 9 -- Index.