Introduction To Machine Learning
by Nils J Nilsson
Number of pages: 209
This book surveys many of the important topics in machine learning circa 1996. The intention was to pursue a middle ground between theory and practice. This book concentrates on the important ideas in machine learning -- it is neither a handbook of practice nor a compendium of theoretical proofs. The goal was to give the reader sufficient preparation to make the extensive literature on machine learning accessible.
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by G. James, D. Witten, T. Hastie, R. Tibshirani - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
by Dimitri P. Bertsekas - Athena Scientific
The book considers large and challenging multistage decision problems, which can be solved by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations.
by Ratnadip Adhikari, R. K. Agrawal - arXiv
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
by Gianluca Bontempi, Souhaib Ben Taieb - OTexts
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.