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  • Understanding Machine Learning: From Theory to Algorithms

    This book explains the principles behind the automated learning approach and the considerations underlying its usage. It provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

  • Building Machine Learning Systems with Python (Willi Richert)

    Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. You'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python.

  • Introduction to Machine Learning (Alex Smola, et al)

    This book is a introductory textbook on the subject, discussesing many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

  • Reinforcement Learning: An Introduction, Second Edition

    This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required.

  • A Course in Machine Learning (Hal Daume III)

    This 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.).

  • Statistical Foundations of Machine Learning (Gianluca Bontempi)

    This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

  • Machine Learning and Data Mining (Aaron Hertzmann)

    This is an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. It offers a grounding in machine learning concepts as well as practical advice on techniques in real-world data mining.

  • Efficient Learning Machines: Theories, Concepts, and Applications

    This book explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.

  • Deep Learning Tutorials (LISA Lab)

    The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

  • Gaussian Processes for Machine Learning (Carl E. Rasmussen)

    This book provides a long-needed systematic and unified treatment of theoretical and practical aspects of Gaussian Processes (GPs) in machine learning. It deals with the supervised-learning problem for both regression and classification.

  • New Advances in Machine Learning (Yagang Zhang)

    This book provides an up-to-date and systematical introduction to the principles and algorithms of machine learning, as well as a good introduction to many approaches of machine learning, and the source of useful bibliographical information.

  • Application of Machine Learning (Yagang Zhang)

    Present the latest applications of machine learning, which mainly include: speech recognition, traffic and fault classification, surface quality prediction in laser machining, network security and bioinformatics, enterprise credit risk evaluation, and so on.

  • Numerical Algorithms: Computer Vision, Machine Learning, etc.

    This book presents a new approach to numerical analysis for modern computer scientists, covers a wide range of topics - from numerical linear algebra to optimization and differential equations - focusing on real-world motivation and unifying themes.

  • Inductive Logic Programming: Techniques and Applications

    This book is an introduction to inductive logic programming (ILP), which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs.

  • Learning Deep Architectures for AI (Yoshua Bengio)

    This book discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed.

  • Neural Networks and Deep Learning (Michael Nielsen)

    Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

  • Machine Learning, Neural and Statistical Classification (D. Michie)

    Statistical, machine learning and neural network approaches to classification are all covered in this volume.

  • Machine Learning: The Complete Guide (Wikipedia)

    This book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. It also covers a wide array of central topics that have not been addressed by other books.

  • The LION Way: Machine Learning Plus Intelligent Optimization

    This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to people in both fields. Optimization approaches have enjoyed prominence in machine learning.

  • Theory and Novel Applications of Machine Learning (Meng Joo Er)

    This books reports the latest developments and futuristic trends in Machine Learning. It involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc.

  • Machine Learning Using C# Succinctly (James McCaffrey)

    This book shows several different approaches to applying machine learning to data analysis and prediction problems. It also demonstrates different clustering and classification techniques, and explains how effective these techniques can be.

  • Computer Vision: Models, Learning, and Inference (Simon Prince)

    The book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. The detailed methodological presentation is useful for practitioners of computer vision.

  • Robot Learning (Suraiya Jabin)

    It gives a focused introduction to the primary themes in a Robot learning course and demonstrates the relevance and practicality of various Machine Learning algorithms to a wide variety of real-world applications from evolutionary techniques to reinforcement learning, classification, control, uncertainty and many other important fields.

  • Machine Learning (Yagang Zhang)

    This book presents today�s state and development tendencies of machine learning. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications.

  • Machine Learning (Abdelhamid Mellouk, et al)

    The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

  • An Introduction to Statistical Learning: with Applications in R

    It provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

  • The Elements of Statistical Learning: Data Mining, Inference, etc.

    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.

  • Information Theory, Inference, and Learning Algorithms (D MacKay)

    This textbook introduces Information theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.

  • AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java

    Illustrateing how to program AI algorithms in Lisp, Prolog, and Java. Topics include: simple production-like system based on logic, logic-based learning, and natural language parsing.

  • Recent Advances in Face Recognition (Kresimir Delac, et al)

    This book provides a broad overview on face recognition and identified trends for future developments and the means for implementing robust systems..

  • From Bricks to Brains: Embodied Cognitive Science of LEGO Robots

    This book introduces embodied cognitive science and illustrates its foundational ideas through the construction and observation of LEGO Mindstorms robots.

  • Mind, Body, World: Foundations of Cognitive Science (M. Dawson)

    Intended to introduce both graduate and senior undergraduate students to the foundations of cognitive science, Mind, Body, World addresses a number of questions currently being asked by those practicing in the field.

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