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Bibliografía Comentada de ML

Última modificación: 19 de Mayo de 2017, y ha tenido 141 vistas

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Título:
Metaheuristics: From Design to Implementation
Descripción:

This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code.

Tipo: Libro

Título:
Understanding Machine Learning: From Theory to Algorithms
Descripción:

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Tipo: Libro

Título:
An Introduction to Statistical Learning with Applications in R
Descripción:

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Tipo: Libro

Título:
Data Mining: The Textbook
Descripción:

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. 

Tipo: Libro

Título:
Introduction to Machine Learning (Ethem Alpaydin)
Descripción:

The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises.

Tipo: Libro

Título:
Learning from Data (Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin)
Descripción:

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. In addition, our readers are given free access to online e-Chapters that we update with the current trends in Machine Learning, such as deep learning and support vector machines. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. What we have emphasized are the necessary fundamentals that give any student of learning from data a solid foundation. 

Tipo: Libro

Título:
Probability for Statistics and Machine Learning
Descripción:

This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.

Tipo: Libro

Título:
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Pedro Domingos)
Descripción:

Wonderfully erudite, humorous, and easy to read, the Master Algorithm by top Machine Learning researcher Pedro Domingos takes you on a journey to visit the 5 tribes of Machine Learning experts and helps you understand what the Master Algorithm can be.

Tipo: Libro

Título:
A Few Useful Things to Know about Machine Learning
Descripción:

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.

Tipo: Artículo

Título:
Probability Theory Review for Machine Learning
Descripción:

Broadly speaking, probability theory is the mathematical study of uncertainty. It plays a central role in machine learning, as the design of learning algorithms often relies on probabilistic assumption of the data. This set of notes attempts to cover some basic probability theory that serves as a background for the class.

Tipo: Otro

Título:
Understanding Random Forests (Tesis de Gilles Louppe)
Descripción:

Accordingly, the goal of this thesis is to provide an in-depth analysis of random forests, consistently calling into question each and every part of the algorithm, in order to shed new light on its learning capabilities, inner workings and interpretability.

Tipo: Otro

Título:
Probability Cheat-Sheet v2.0 (de William Chen y Jose Blitzstein)
Descripción:

Compiled by William Chen (http://wzchen.com) and Joe Blitzstein, with contributions from Sebastian Chiu, Yuan Jiang, Yuqi Hou, and Jessy Hwang. Material based on Joe Blitzstein’s (@stat110) lectures (http://stat110.net) and Blitzstein/Hwang’s Introduction to Probability textbook (http://bit.ly/introprobability). Licensed under CC BY-NC-SA 4.0. Please share comments, suggestions, and errors at http://github.com/wzchen/probability_cheatsheet.

Tipo: Otro

Título:
Learning from Data: Online Course
Descripción:

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.

Tipo: Video-Curso

Título:
Deep Learning (Google en Udacity)
Descripción:

We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

Tipo: Video-Curso

Título:
ML Foundations and ML Techniques
Descripción:

Transparencias de los dos cursos MOOC que se imparten en Coursera.... por desgracia, los videos están en chino mandarín, pero las transparencias están en inglés y son bastante buenas.

Tipo: Video-Curso

Título:
Caffe
Descripción: Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Tipo: Otro

Título:
CS231n: Convolutional Neural Networks for Visual Recognition.
Descripción:

This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Tipo: Video-Curso

Título:
Using convolutional neural nets to detect facial keypoints tutorial
Descripción:

This is a hands-on tutorial on deep learning. Step by step, we'll go about building a solution for the Facial Keypoint Detection Kaggle challenge. The tutorial introduces Lasagne, a new library for building neural networks with Python and Theano. We'll use Lasagne to implement a couple of network architectures, talk about data augmentation, dropout, the importance of momentum, and pre-training. Some of these methods will help us improve our results quite a bit.

Tipo: Tutorial

Título:
Deep Learning TextBook
Descripción:

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The print version will be available for sale soon.

Tipo: Libro

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