Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. - Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics - Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study - Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi'an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

• Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
• Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
• Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

1. Introduction
2. Mathematical Foundations
3. Data Fitting and Method of Least Squares
4. Logistic Regression and PCA
5. Data Mining
6. Artificial Neural Networks
7. Support Vector Machine
8. Deep Learning