Introduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

Produk Detail:

  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann
  • Pages : 534 pages
  • ISBN : 0128023503
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKIntroduction to Statistical Machine Learning

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning
  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann
  • Release : 31 October 2015
GET THIS BOOKIntroduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer Science & Business Media
  • Release : 24 June 2013
GET THIS BOOKAn Introduction to Statistical Learning

An Introduction to Statistical Learning 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

Introduction to Statistical and Machine Learning Methods for Data Science

Introduction to Statistical and Machine Learning Methods for Data Science
  • Author : Carlos Andre Reis Pinheiro,Mike Patetta
  • Publisher : SAS Institute
  • Release : 06 August 2021
GET THIS BOOKIntroduction to Statistical and Machine Learning Methods for Data Science

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book

A Concise Introduction to Machine Learning

A Concise Introduction to Machine Learning
  • Author : A.C. Faul
  • Publisher : CRC Press
  • Release : 23 August 2019
GET THIS BOOKA Concise Introduction to Machine Learning

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

An Elementary Introduction to Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory
  • Author : Sanjeev Kulkarni,Gilbert Harman
  • Publisher : John Wiley & Sons
  • Release : 09 June 2011
GET THIS BOOKAn Elementary Introduction to Statistical Learning Theory

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security
  • Author : Mark Stamp
  • Publisher : CRC Press
  • Release : 22 September 2017
GET THIS BOOKIntroduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It

Introduction to Machine Learning

Introduction to Machine Learning
  • Author : Ethem Alpaydin
  • Publisher : MIT Press
  • Release : 29 August 2014
GET THIS BOOKIntroduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in

The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOKThe Elements of Statistical Learning

During the past decade there has been an explosion in computation and information technology. With it have 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

Introduction to Statistical Relational Learning

Introduction to Statistical Relational Learning
  • Author : Lise Getoor,Ben Taskar
  • Publisher : MIT Press
  • Release : 17 May 2022
GET THIS BOOKIntroduction to Statistical Relational Learning

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging

Statistical Machine Learning

Statistical Machine Learning
  • Author : Richard Golden
  • Publisher : CRC Press
  • Release : 24 June 2020
GET THIS BOOKStatistical Machine Learning

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning
  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann Publishers
  • Release : 12 October 2015
GET THIS BOOKIntroduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

Statistical Reinforcement Learning

Statistical Reinforcement Learning
  • Author : Masashi Sugiyama
  • Publisher : CRC Press
  • Release : 16 March 2015
GET THIS BOOKStatistical Reinforcement Learning

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amo

Machine Learning and Data Science

Machine Learning and Data Science
  • Author : Daniel D. Gutierrez
  • Publisher : Technics Publications
  • Release : 01 November 2015
GET THIS BOOKMachine Learning and Data Science

A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set

Introduction to Machine Learning with R

Introduction to Machine Learning with R
  • Author : Scott V. Burger
  • Publisher : "O'Reilly Media, Inc."
  • Release : 07 March 2018
GET THIS BOOKIntroduction to Machine Learning with R

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity