1. Bayesian Reasoning and Machine Learning by David Barber
Bayesian Reasoning and Machine Learning by David Barber is an ebook that is designed for final-year undergraduates and master’s students with limited background in linear algebra and calculus. From basic reasoning to advanced techniques within the framework of graphical models, readers get to learn the developing skills as easily as they could wish.
2. Inductive Logic Programming: Techniques and Applications
by Nada Lavrac, Saso Dzeroski
This book on inductive logic programming (ILP) that a research field at the intersection of machine learning and logic programming aims at a formal framework besides providing the practical algorithms for inductively learning relational descriptions in the form of logic programs.
3. Gaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams
This book makes readers learn a principled, practical, probabilistic approach to learning in kernel machines in quite a different and easy way. It comprises of supervised-learning problem for regression and classification, and includes detailed algorithms.
4. Machine Learning, Neural and Statistical Classification by D. Michie, D. J. Spiegelhalter
This book by D.Michie, D. J. Spiegelhalter is written with the aim to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets and also draw conclusions on their applicability to realistic industrial problems.
5. Information Theory, Inference, and Learning Algorithms by David J. C. MacKay
In this book Information theory topic is explained and well talked about in detail to help readers attain good knowledge about the practical communication systems like arithmetic coding for data compression and sparse-graph codes for error-correction.
6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction BY by T. Hastie, R. Tibshirani, J. Friedman
With this book, readers get to learn conceptual underpinnings rather than just obtaining theorical knowledge. It comprises of statistical framework and is the best pick for statisticians, researchers and practitioners.
7. The LION Way by Roberto Battiti, Mauro Brunato
The LION Way is an ebook that is written with the aim to help readers in machine learning and Intelligent Optimization (LION) that is the combination of learning from data and optimization applied to solve complex and dynamic problems.
8. Introduction to Machine Learning by Amnon Shashua
Introduction to Machine learning is an ebook that comprises of Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering) and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
9. A Course in Machine Learning by Hal Daume III
A Course in Machine Learning is an ebook that comprises of a set of introductory material covering various aspects of modern machine learning.
10. Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer
The book comprises of know how of reinforcement learning. There are 22 chapters in all. While first 11 chapters focus on description and extended scope of reinforcement learning, the remaining 11 chapters show that there is already wide usage in numerous fields.
11. Introduction To Machine Learning by Nils J Nilsson
As the name says, this is an introduction to machine learning. The book by Nils J Nilsson surveys topics in machine learning circa 1996 with the aim to pursue a middle ground between theory and practice.
12. Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto
Reinforcement learning lets users learn machine in an easy way. Its like an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment.