Reading
Books, slides and presentations
Textbooks, free books and lecture slides recommended across the salon.
Machine Learning in Action, Peter Harrington, 2012
Chapter 1 and 7 are available for free on the publisher website
Neural Networks and Deep Learning, 2014
Neural Networks and Deep Learning is a free online book. The book will teach you about:
Neural networks, a beautiful biologically inspired programming paradigm which enables a computer to learn from observational data
Deep learning, a powerful set of techniques for learning in neural networks
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.
The book is currently an incomplete beta draft. More chapters will be added over the coming months. For now, you can:
Read Chapter 1, which explains how neural networks can learn to recognize handwriting
Read Chapter 2, which explains backpropagation, the most important algorithm used to learn in neural networks.
neuralnetworksanddeeplearning.com
Gaussian processes for Machine Learning, C. Rasmussen and C. Williams, 2006
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine learning community over the past decade, and this book provides a long needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well known techniques from machine learning and statistics are discussed, including support vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman, 2009
During the past decade has been an explosion in computation and information technology. With it has 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. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non negative matrix factorization and spectral clustering. There is also a chapter on methods for ``wide'' data (italics p bigger than n), including multiple testing and false discovery rates.
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
Chapter 8, Graphical Models
Probabilities play a central role in modern pattern recognition. We have seen in Chapter 1 that probability theory can be expressed in terms of two simple equations corresponding to the sum rule and the product rule. All of the probabilistic infer ence and learning manipulations discussed in this book, no matter how complex, amount to repeated application of these two equations. We could therefore proceed to formulate and solve complicated probabilistic models purely by algebraic ma nipulation. However, we shall find it highly advantageous to augment the analysis using diagrammatic representations of probability distributions, called probabilistic graphical models. These offer several useful properties:
1. They provide a simple way to visualize the structure of a probabilistic model and can be used to design and motivate new models.
2. Insights into the properties of the model, including conditional independence properties, can be obtained by inspection of the graph.
3. Complex computations, required to perform inference and learning in sophis ticated models, can be expressed in terms of graphical manipulations, in which underlying mathematical expressions are carried along implicitly.
Bayesian Reasoning and Machine Learning, David Barber, 2012 (online version 02 2014)
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. 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. Students learn more than a menu of techniques, they develop analytical and problem solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Information Theory, Inference, and Learning Algorithms, David McKay, 2003
Introduction to Machine Learning, Alex Smola, S.V.N. Vishwanathan, 2008
Free Book List
ebooks for free online viewing and/or download
Free resource book (need to sign in)
There are too many machine learning resources on the internet, so much so that it can feel overwhelming.
I have read the books and taken the courses and can give you good advice on where to start.
Resources you can use to learn faster
I have hand picked the best machine learning…
…books
…websites
…videos
…university courses
…software
…competition sites
These resources have been listed in a handy PDF that you can download now
Free ML ebooks on it ebooks, please read stackoverflow before accessing to this website by yourself
A course in Machine Learning by Hal Daume, 2012
Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning.
CIML 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.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
Learning Deep Architecture for AI by Yoshua Bengio, 2009
Abstract
Theoretical results suggest that in order to learn the kind of com plicated functions that can represent high level abstractions (e.g., in vision, language, and other AI level tasks), one may need deep architec tures. Deep architectures are composed of multiple levels of nonlinear operations, such as in neural nets with many hidden layers or in com plicated propositional formulae re using many sub formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state of the art in certain areas. This monograph discusses the motivations and principles regarding 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 such as Deep Belief Networks.
Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson Pilon, 2014
Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding first, and mathematics second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.
Blog recommending useful books
A blog written in Chinese which introduces and recommends many useful ML books (the books are mostly written in English).
Textbook for Statistics
Introduction to Pattern recognition
Translated version of Machine Learning by Tom Mitchell:
More to be added ...
SLIDES/PRESENTATIONS
Meetup's Presentations
Slideshare.com
Slides.com
More to be added ...
The
Machine Learning
Salon
Download
Menu