Introduction to Algorithms for Data Mining and Machine Learning

Data mining and machine learning draw on more than algorithms alone: sound results depend on optimization, probability, statistical modeling, and a clear understanding of how methods behave. Xin-She Yang’s Introduction to Algorithms for Data Mining and Machine Learning brings those foundations together with the techniques used to analyze data and build predictive models.

The book moves from core mathematical ideas to applications, making it a useful point of reference for students and readers building a structured understanding of the field.

Start with the ideas behind the algorithms

The opening chapters establish the groundwork: optimization, convexity, computational complexity, norms and regularization, probability distributions, Bayesian models, Monte Carlo sampling, and measures such as entropy and KL divergence. This context helps readers see not just what an algorithm does, but the mathematical concepts that support it.

From regression to data mining

Yang then surveys methods for fitting and interpreting data, including linear and nonlinear regression, logistic and softmax regression, principal component analysis, linear discriminant analysis, and independent component analysis. Later chapters take up clustering, k-nearest neighbors, k-means, decision trees, random forests, Bayesian classifiers, and mining large datasets.

Explore optimization and learning methods

Coverage ranges from gradient-based and gradient-free optimization to evolutionary algorithms and swarm intelligence. Support vector machines, support vector regression, artificial neural networks, backpropagation, deep learning, and hyperparameter tuning extend the discussion to widely used approaches in modern machine learning.

A bridge between theory and practice

The book includes worked examples and notes on relevant software packages. Its mathematical explanations are intended to clarify the methods without making proof-based rigor the focus, while the broad selection of techniques helps readers compare approaches across modeling tasks.

Who may find it useful?

Students and practitioners in computer science, management science, economics, and engineering can use this book to build or refresh a foundation in data mining, machine learning, and optimization. It is especially relevant to readers who want to connect the mathematics of modeling with the algorithms used for classification, clustering, curve fitting, and prediction.