Algorithmic Learning in a Random World

Author: Vladimir Vovk
Publisher: Springer Science & Business Media
ISBN: 0387250611
Format: PDF, Docs
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

Algorithmic Learning in a Random World

Author: Vladimir Vovk
Publisher: Springer Science & Business Media
ISBN: 9780387001524
Format: PDF, ePub
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A new scientific monograph developing significant new algorithmic foundations in machine learning theory. Researchers and postgraduates in CS, statistics, and A.I. will find the book an authoritative and formal presentation of some of the most promising theoretical developments in machine learning.

Algorithmic Learning in a Random World

Author: Vladimir Vovk
Publisher: Springer
ISBN: 9781441934710
Format: PDF
Download Now
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

Conformal Prediction for Reliable Machine Learning

Author: Vineeth Balasubramanian
Publisher: Newnes
ISBN: 0124017150
Format: PDF, Kindle
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The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

Probability and Finance

Author: Glenn Shafer
Publisher: John Wiley & Sons
ISBN: 0471461717
Format: PDF, ePub, Mobi
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Provides a foundation for probability based on game theory rather than measure theory. A strong philosophical approach with practical applications. Presents in-depth coverage of classical probability theory as well as new theory.

The Master Algorithm

Author: Pedro Domingos
Publisher: Basic Books
ISBN: 0465061923
Format: PDF, ePub, Mobi
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"Wonderfully erudite, humorous, and easy to read." --KDNuggets In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Format: PDF, Docs
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Data Science Algorithms in a Week

Author: David Natingga
Publisher: Packt Publishing Ltd
ISBN: 1787282740
Format: PDF
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Build strong foundation of machine learning algorithms In 7 days. About This Book Get to know seven algorithms for your data science needs in this concise, insightful guide Ensure you're confident in the basics by learning when and where to use various data science algorithms Learn to use machine learning algorithms in a period of just 7 days Who This Book Is For This book is for aspiring data science professionals who are familiar with Python and have a statistics background. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skill set. What You Will Learn Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series See how to cluster data using the k-Means algorithm Get to know how to implement the algorithms efficiently in the Python and R languages In Detail Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis. This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets. This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. Style and approach Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.

Machine Learning Algorithms

Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1789345480
Format: PDF, ePub, Mobi
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An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.

Machine Learning

Author: Stephen Marsland
Publisher: CRC Press
ISBN: 1498759785
Format: PDF, ePub, Docs
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A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.