Machine Learning Books
In this post we list a set of books that can be useful to the student learning machine learning.
A Course in Machine Learning
A Course in Machine Learning is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). Its focus is on broad applications with a rigorous backbone (Hal Daumé III, 2012).
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. […]This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics (Hastie, T et al., 2001).
Data Mining and Analysis: Fundamental Concepts and Algorithms
This book is an outgrowth of data mining courses at RPI and UFMG; the RPI course has been oﬀered every Fall since 1998, whereas the UFMG course has been oﬀered since 2002. While there are several good books on data mining and related topics, we felt that many of them are either too high-level or too advanced. Our goal was to write an introductory text which focuses on the fundamental algorithms in data mining and analysis (Mohammed Zaki and Wagner Meira Jr, 2014).
Introduction to Data Science, by Jeffrey Stanton, provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics (Jeffrey M. Stanton, 2014).
Advanced Data Analysis from an Elementary Point of View
These are the notes for 36-402, Advanced Data Analysis, at Carnegie Mellon. The class presumes a ﬁrm grasp on linear algebra and multivariable calculus, and that you can read and write simple functions in R (Cosma R. Shalizi, 2012).
Bayesian Reasoning and Machine Learning
This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master’s students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models (David Barber, 2012).
Applied Data Science
The purpose of this course is to take people with strong mathematical/statistical knowledge and teach them software development fundamentals (Ian Langmore Daniel Krasner, 2014).
A First Encounter with Machine Learning
“Much of machine learning is build upon concepts from mathematics such as partial derivatives, eigenvalue decompositions, multivariate probability densities and so on. I quickly found that these concepts could not be taken for granted at an undergraduate level. The situation was aggravated by the lack of a suitable textbook. Excellent textbooks do exist for this ﬁeld, but I found all of them to be too technical for a ﬁrst encounter with machine learning. This experience led me to believe there was a genuine need for a simple, intuitive introduction into the concepts of machine learning. A ﬁrst read to wet the appetite so to speak, a prelude to the more technical and advanced textbooks” (Welling, 2011).
Hastie, T.; Tibshirani, R. & Friedman, J. (2001), The Elements of Statistical Learning , Springer New York Inc. , New York, NY, USA .
Hal Daumé III. (2012). A Course in Machine Learning.
Data Mining and Analysis: Fundamental Concepts and Algorithms (28 February 2014) by Mohammed J. Zaki, Wagner Meira.
Jeffrey M. Stanton. Introduction to Data Science: Using the R Language for Statistical Computing and Graphics
Advanced Data Analysis from an Elementary Point of View (2012) by Cosma R. Shalizi.
David Barber. 2012. Bayesian Reasoning and Machine Learning. Cambridge University Press, New York, NY, USA.
Ian Langmore Daniel Krasner. (Accessed 2014) Applied Data Science. Columbia University.
Max Welling . (2011).A First Encounter with Machine Learning.