## subcategories

**Deep Learning** (6)

## e-books in Machine Learning category

**Machine Learning: A Probabilistic Perspective**

by

**Kevin Patrick Murphy**-

**The MIT Press**,

**2012**

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

(

**1379**views)

**Introduction to Machine Learning for the Sciences**

by

**Titus Neupert, et al.**-

**arXiv.org**,

**2021**

This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich. We discuss supervised, unsupervised, and reinforcement learning.

(

**1061**views)

**Foundations of Machine Learning**

by

**M. Mohri, A. Rostamizadeh, A. Talwalkar**-

**The MIT Press**,

**2018**

This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.

(

**3889**views)

**The Hundred-Page Machine Learning Book**

by

**Andriy Burkov**,

**2019**

This is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.

(

**4243**views)

**Reinforcement Learning and Optimal Control**

by

**Dimitri P. Bertsekas**-

**Athena Scientific**,

**2019**

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.

(

**6107**views)

**An Introduction to Probabilistic Programming**

by

**Jan-Willem van de Meent, et al.**-

**arXiv.org**,

**2018**

This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.

(

**3312**views)

**A Brief Introduction to Machine Learning for Engineers**

by

**Osvaldo Simeone**-

**arXiv.org**,

**2017**

This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.

(

**4464**views)

**Machine Learning for Data Streams**

by

**Albert Bifet, et al.**-

**The MIT Press**,

**2017**

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.

(

**4293**views)

**Elements of Causal Inference: Foundations and Learning Algorithms**

by

**J. Peters, D. Janzing, B. Schölkopf**-

**The MIT Press**,

**2017**

This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...

(

**3881**views)

**Machine Learning for Designers**

by

**Patrick Hebron**-

**O'Reilly Media**,

**2016**

This book introduces you to contemporary machine learning systems and helps you integrate machine-learning capabilities into your user-facing designs. Patrick Hebron explains how machine-learning applications can affect the way you design websites.

(

**4711**views)

**Boosting: Foundations and Algorithms**

by

**Robert E. Schapire, Yoav Freund**-

**The MIT Press**,

**2014**

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.

(

**4632**views)

**Optimal and Learning Control for Autonomous Robots**

by

**Jonas Buchli, et al.**-

**arXiv.org**,

**2017**

The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.

(

**4229**views)

**Modeling Agents with Probabilistic Programs**

by

**Owain Evans, et al.**-

**AgentModels.org**,

**2017**

This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.

(

**4153**views)

**Statistical Foundations of Machine Learning**

by

**Gianluca Bontempi, Souhaib Ben Taieb**,

**2017**

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.

(

**7164**views)

**Statistical Learning and Sequential Prediction**

by

**Alexander Rakhlin, Karthik Sridharan**-

**University of Pennsylvania**,

**2014**

This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...

(

**4963**views)

**Understanding Machine Learning: From Theory to Algorithms**

by

**Shai Shalev-Shwartz, Shai Ben-David**-

**Cambridge University Press**,

**2014**

This book introduces machine learning and the algorithmic paradigms it offers. It provides a theoretical account of the fundamentals underlying machine learning and mathematical derivations that transform these principles into practical algorithms.

(

**7202**views)

**Lecture Notes in Machine Learning**

by

**Zdravko Markov**-

**Central Connecticut State University**,

**2003**

Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning ...

(

**7507**views)

**Machine Learning and Data Mining: Lecture Notes**

by

**Aaron Hertzmann**-

**University of Toronto**,

**2010**

Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.

(

**7934**views)

**Learning Deep Architectures for AI**

by

**Yoshua Bengio**-

**Now Publishers**,

**2009**

This book discusses the principles of learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models.

(

**5807**views)

**An Introduction to Statistical Learning**

by

**G. James, D. Witten, T. Hastie, R. Tibshirani**-

**Springer**,

**2013**

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.

(

**7843**views)

**Algorithms for Reinforcement Learning**

by

**Csaba Szepesvari**-

**Morgan and Claypool Publishers**,

**2009**

We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.

(

**6023**views)

**A Survey of Statistical Network Models**

by

**A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi**-

**arXiv**,

**2009**

We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.

(

**6086**views)

**Introduction to Machine Learning**

by

**Alex Smola, S.V.N. Vishwanathan**-

**Cambridge University Press**,

**2008**

Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.

(

**7397**views)

**An Introductory Study on Time Series Modeling and Forecasting**

by

**Ratnadip Adhikari, R. K. Agrawal**-

**arXiv**,

**2013**

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.

(

**9438**views)

**The LION Way: Machine Learning plus Intelligent Optimization**

by

**Roberto Battiti, Mauro Brunato**-

**Lionsolver, Inc.**,

**2013**

Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex problems. This book is about increasing the automation level and connecting data directly to decisions and actions.

(

**30313**views)

**A Course in Machine Learning**

by

**Hal Daumé III**-

**ciml.info**,

**2012**

Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.

(

**18058**views)

**A First Encounter with Machine Learning**

by

**Max Welling**-

**University of California Irvine**,

**2011**

The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A prelude to the more advanced text books.

(

**8933**views)

**Bayesian Reasoning and Machine Learning**

by

**David Barber**-

**Cambridge University Press**,

**2011**

The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.

(

**19457**views)

**Introduction to Machine Learning**

by

**Amnon Shashua**-

**arXiv**,

**2009**

Introduction to Machine learning covering 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).

(

**19679**views)

**The Elements of Statistical Learning: Data Mining, Inference, and Prediction**

by

**T. Hastie, R. Tibshirani, J. Friedman**-

**Springer**,

**2009**

This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.

(

**37037**views)

**Reinforcement Learning**

by

**C. Weber, M. Elshaw, N. M. Mayer**-

**InTech**,

**2008**

This book describes and extends the scope of reinforcement learning. It also shows that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional controllers.

(

**19019**views)

**Machine Learning**

by

**Abdelhamid Mellouk, Abdennacer Chebira**-

**InTech**,

**2009**

Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.

(

**14047**views)

**Reinforcement Learning: An Introduction**

by

**Richard S. Sutton, Andrew G. Barto**-

**The MIT Press**,

**2017**

The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.

(

**24198**views)

**Gaussian Processes for Machine Learning**

by

**Carl E. Rasmussen, Christopher K. I. Williams**-

**The MIT Press**,

**2005**

Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.

(

**25163**views)

**Machine Learning, Neural and Statistical Classification**

by

**D. Michie, D. J. Spiegelhalter**-

**Ellis Horwood**,

**1994**

The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.

(

**25083**views)

**Introduction To Machine Learning**

by

**Nils J Nilsson**,

**1997**

This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.

(

**26299**views)

**Inductive Logic Programming: Theory and Methods**

by

**Stephen Muggleton, Luc de Raedt**-

**ScienceDirect**,

**1994**

Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.

(

**31660**views)

**Practical Artificial Intelligence Programming in Java**

by

**Mark Watson**-

**Lulu.com**,

**2008**

The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).

(

**21658**views)

**Information Theory, Inference, and Learning Algorithms**

by

**David J. C. MacKay**-

**Cambridge University Press**,

**2003**

A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.

(

**25516**views)