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Tag: machine learning: an applied mathematics introduction

  • An Introduction to Conditional Random Fields (Foundations and Trends(r) in Machine Learning)


    Price: $85.00 – $77.49
    (as of Jan 22,2025 06:51:39 UTC – Details)




    Publisher ‏ : ‎ Now Publishers (August 23, 2012)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 120 pages
    ISBN-10 ‏ : ‎ 160198572X
    ISBN-13 ‏ : ‎ 978-1601985729
    Item Weight ‏ : ‎ 6.2 ounces
    Dimensions ‏ : ‎ 6.14 x 0.25 x 9.21 inches


    Conditional Random Fields (CRFs) are a popular and powerful framework for modeling structured prediction tasks in machine learning. In this post, we will provide an introduction to CRFs, focusing on the foundational concepts and recent advances in the field.

    CRFs are a type of probabilistic graphical model that captures dependencies between input variables and output variables. Unlike traditional models such as hidden Markov models or naive Bayes classifiers, CRFs allow for complex and flexible modeling of structured data, such as sequences, graphs, or images.

    One of the key advantages of CRFs is their ability to model correlations between neighboring variables in the output space. This makes them particularly well-suited for tasks such as natural language processing, speech recognition, and computer vision, where the output is inherently structured and sequential.

    In recent years, there has been significant progress in developing efficient algorithms for training and inference in CRFs, making them a practical choice for a wide range of machine learning applications. Additionally, researchers have explored extensions and generalizations of CRFs, such as deep CRFs and hierarchical CRFs, to further improve their performance and applicability.

    Overall, CRFs are a versatile and powerful tool for modeling structured prediction tasks, and understanding their foundations and recent developments can be valuable for both researchers and practitioners in the field of machine learning. If you are interested in learning more about CRFs, we recommend checking out the book “Conditional Random Fields (Foundations and Trends® in Machine Learning)” for a comprehensive overview of the topic.
    #Introduction #Conditional #Random #Fields #Foundations #Trendsr #Machine #Learning,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning with R: Rigorous Mathematical Analysis by Burge



    Introduction to Machine Learning with R: Rigorous Mathematical Analysis by Burge

    Price : 49.45

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    Introduction to Machine Learning with R: Rigorous Mathematical Analysis by Burge

    Machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. In recent years, machine learning has become increasingly popular in various industries, including finance, healthcare, and technology.

    If you are interested in delving deeper into the mathematical foundations of machine learning and how they are implemented in R, then “Introduction to Machine Learning with R: Rigorous Mathematical Analysis” by Burge is the perfect resource for you. This book provides a comprehensive introduction to the theory and practice of machine learning, with an emphasis on the mathematical principles that underlie the algorithms.

    Burge covers a wide range of topics, including linear regression, logistic regression, decision trees, support vector machines, and neural networks. Each topic is explained in detail, with a focus on the mathematical concepts and algorithms that drive these machine learning techniques.

    Whether you are a beginner looking to learn the basics of machine learning or an experienced data scientist looking to deepen your understanding of the mathematical foundations, “Introduction to Machine Learning with R: Rigorous Mathematical Analysis” by Burge is a valuable resource that will enhance your knowledge and skills in this rapidly growing field.
    #Introduction #Machine #Learning #Rigorous #Mathematical #Analysis #Burge,machine learning: an applied mathematics introduction

  • An Introduction to IoT Analytics (Chapman & Hall/CRC Data Science Series)


    Price: $38.44
    (as of Jan 22,2025 05:48:56 UTC – Details)




    ASIN ‏ : ‎ B091GN3VSK
    Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (March 31, 2021)
    Publication date ‏ : ‎ March 31, 2021
    Language ‏ : ‎ English
    File size ‏ : ‎ 14347 KB
    Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 372 pages
    Format ‏ : ‎ Print Replica


    Are you interested in learning more about IoT analytics? Look no further than “An Introduction to IoT Analytics,” part of the Chapman & Hall/CRC Data Science Series. This comprehensive guide covers everything you need to know about analyzing data from Internet of Things (IoT) devices, including sensors, actuators, and other connected devices.

    Written by experts in the field, this book provides a deep dive into the tools, techniques, and best practices for extracting insights from IoT data. Whether you’re a beginner looking to understand the basics or an experienced data scientist wanting to expand your skills, this book has something for everyone.

    Topics covered in “An Introduction to IoT Analytics” include data collection and preprocessing, real-time analytics, machine learning algorithms, and visualization techniques. The book also explores case studies and practical examples to help you apply these concepts in real-world scenarios.

    Don’t miss out on this essential resource for anyone working with IoT data. Order your copy of “An Introduction to IoT Analytics” today and take your analytics skills to the next level!
    #Introduction #IoT #Analytics #Chapman #HallCRC #Data #Science #Series,machine learning: an applied mathematics introduction

  • Introduction To Electrical Engineering


    Price: $128.75 – $25.55
    (as of Jan 22,2025 03:45:34 UTC – Details)




    Publisher ‏ : ‎ McGraw-Hill Science/Engineering/Math; 2nd edition (January 1, 1992)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 816 pages
    ISBN-10 ‏ : ‎ 007011322X
    ISBN-13 ‏ : ‎ 978-0070113220
    Item Weight ‏ : ‎ 2.5 pounds
    Dimensions ‏ : ‎ 7.4 x 1.2 x 9.3 inches


    Electrical engineering is a fascinating field that deals with the study and application of electricity, electronics, and electromagnetism. It is a branch of engineering that involves designing, developing, testing, and maintaining electrical systems and equipment.

    From power generation and distribution to telecommunications and electronics, electrical engineers play a crucial role in ensuring that our modern world functions smoothly. They work on a wide range of projects, from designing circuits for consumer electronics to developing sustainable energy solutions.

    If you are interested in a career in electrical engineering, you will need a strong foundation in math and physics, as well as a passion for problem-solving and innovation. Electrical engineers must be able to think critically, work well under pressure, and communicate effectively with colleagues and clients.

    In this post, we will explore the basics of electrical engineering, including the fundamental concepts, principles, and applications of this dynamic and rewarding field. Whether you are a student considering a major in electrical engineering or a professional looking to expand your knowledge, this introduction will provide you with valuable insights into the exciting world of electrical engineering. Stay tuned for more in-depth discussions on topics such as circuit analysis, power systems, electronics, and more!
    #Introduction #Electrical #Engineering,machine learning: an applied mathematics introduction

  • MCMC from Scratch: A Practical Introduction to Markov Chain Monte Carlo


    Price: $54.99
    (as of Jan 22,2025 02:44:48 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2022 edition (October 21, 2022)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 203 pages
    ISBN-10 ‏ : ‎ 9811927146
    ISBN-13 ‏ : ‎ 978-9811927140
    Item Weight ‏ : ‎ 1.13 pounds
    Dimensions ‏ : ‎ 6.14 x 0.63 x 9.29 inches


    Markov Chain Monte Carlo (MCMC) is a powerful statistical technique used for sampling from complex probability distributions. In this post, we will provide a practical introduction to MCMC from scratch, covering the basic concepts and steps involved in implementing an MCMC algorithm.

    First, let’s start with the basics. MCMC is a method for generating a sequence of random samples from a target probability distribution. It does this by constructing a Markov chain that has the desired distribution as its stationary distribution. In other words, the samples generated by the Markov chain will eventually converge to the target distribution.

    The key idea behind MCMC is to simulate a Markov chain that explores the target distribution by transitioning from one state to another based on transition probabilities. The chain is constructed in such a way that the distribution of states visited by the chain converges to the target distribution as the number of iterations approaches infinity.

    To implement an MCMC algorithm from scratch, the following steps are typically involved:

    1. Choose a proposal distribution: The proposal distribution determines how the Markov chain transitions from one state to another. It should be easy to sample from and should have a high acceptance rate.

    2. Initialize the Markov chain: Start the chain at a random initial state.

    3. Generate a proposal state: Sample a new state from the proposal distribution.

    4. Calculate the acceptance probability: Compute the acceptance probability based on the ratio of the target distribution at the proposed state and the current state.

    5. Accept or reject the proposal: Accept the proposal with probability equal to the acceptance probability. If the proposal is rejected, stay at the current state.

    6. Update the Markov chain: Move to the accepted state and repeat the process.

    By iterating through these steps, the Markov chain will eventually converge to the target distribution, and the samples generated by the chain can be used for inference and estimation.

    In conclusion, Markov Chain Monte Carlo is a versatile and powerful technique for sampling from complex probability distributions. By understanding the basic concepts and steps involved in implementing an MCMC algorithm from scratch, you can leverage this technique in various statistical and machine learning applications.
    #MCMC #Scratch #Practical #Introduction #Markov #Chain #Monte #Carlo,machine learning: an applied mathematics introduction

  • Statistical Analysis of Network Data with R (Use R! Book 65)


    Price: $45.97
    (as of Jan 22,2025 01:45:05 UTC – Details)




    ASIN ‏ : ‎ B00PULZXI8
    Publisher ‏ : ‎ Springer; 2014th edition (May 22, 2014)
    Publication date ‏ : ‎ May 22, 2014
    Language ‏ : ‎ English
    File size ‏ : ‎ 7776 KB
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 220 pages
    Format ‏ : ‎ Print Replica


    Statistical Analysis of Network Data with R: A Comprehensive Guide (Use R! Book 65)

    In today’s interconnected world, network data plays a crucial role in various fields such as social sciences, biology, finance, and more. Analyzing and interpreting this complex data can be challenging, but with the right tools and techniques, it can lead to valuable insights and discoveries.

    The book “Statistical Analysis of Network Data with R” (Use R! Book 65) provides a comprehensive guide to working with network data using the popular programming language R. Written by experts in the field, this book covers everything from basic network concepts to advanced statistical techniques for analyzing and visualizing network data.

    Whether you are a beginner looking to learn the basics of network analysis or an experienced data scientist wanting to expand your skills, this book has something for everyone. With step-by-step tutorials, real-world examples, and practical exercises, you will learn how to:

    – Import and manipulate network data in R
    – Calculate network metrics and centrality measures
    – Perform statistical tests on network data
    – Visualize networks using various plotting techniques
    – Detect communities and clusters in network data
    – Predict links and edges in networks

    By the end of this book, you will have a solid understanding of network analysis and be able to apply your skills to real-world problems. So why wait? Dive into the world of network data analysis with R and unlock the potential of your data today!
    #Statistical #Analysis #Network #Data #Book,machine learning: an applied mathematics introduction

  • Introduction to Lattice Theory with Computer Science Applications


    Price: $61.74
    (as of Jan 22,2025 00:44:39 UTC – Details)




    ASIN ‏ : ‎ B0104CCRW2
    Publisher ‏ : ‎ Wiley; 1st edition (June 10, 2015)
    Publication date ‏ : ‎ June 10, 2015
    Language ‏ : ‎ English
    File size ‏ : ‎ 15441 KB
    Simultaneous device usage ‏ : ‎ Up to 3 simultaneous devices, per publisher limits
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 227 pages


    Lattice theory is a branch of mathematics that deals with the study of lattices, which are partially ordered sets that have a unique supremum (least upper bound) and infimum (greatest lower bound) for any pair of elements. Lattices have applications in various areas of computer science, such as formal verification, data analysis, and cryptography.

    In this post, we will provide an introduction to lattice theory, explaining its basic concepts and properties, and exploring some of its applications in computer science.

    Basic Concepts of Lattice Theory:
    1. Partial Order: A partially ordered set is a set in which any two elements can be compared with respect to a given relation, typically denoted by ≤. This relation must be reflexive, transitive, and antisymmetric.

    2. Lattice: A lattice is a partially ordered set in which every pair of elements has a unique supremum (least upper bound) and infimum (greatest lower bound). Lattices can be classified as bounded, distributive, modular, and complemented.

    3. Join and Meet Operations: In a lattice, the supremum of two elements is called the join, denoted by a ∨ b, while the infimum is called the meet, denoted by a ∧ b.

    4. Distributivity: A lattice is distributive if the distributive law holds: a ∧ (b ∨ c) = (a ∧ b) ∨ (a ∧ c) and a ∨ (b ∧ c) = (a ∨ b) ∧ (a ∨ c).

    Applications in Computer Science:
    1. Formal Verification: Lattice theory is used in formal verification to model states and transitions in a system, enabling the verification of properties such as safety, liveness, and fairness.

    2. Data Analysis: Lattices are used in data analysis to represent hierarchical structures, such as taxonomies and concept hierarchies, and to perform operations such as aggregation, filtering, and clustering.

    3. Cryptography: Lattices are employed in cryptographic schemes, such as lattice-based cryptography, which offers security against quantum attacks and enables the construction of post-quantum secure encryption and digital signature algorithms.

    In conclusion, lattice theory is a fundamental mathematical concept with diverse applications in computer science. By understanding the basic principles of lattices and their properties, computer scientists can leverage this knowledge to solve complex problems in various domains. Stay tuned for more posts on lattice theory and its practical implications in computer science.
    #Introduction #Lattice #Theory #Computer #Science #Applications,machine learning: an applied mathematics introduction

  • Differential Equations: An Introduction with Mathematica® (Undergraduate Texts in Mathematics)


    Price: $86.74
    (as of Jan 21,2025 23:40:29 UTC – Details)




    Publisher ‏ : ‎ Springer; 2nd edition (August 3, 2004)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 448 pages
    ISBN-10 ‏ : ‎ 0387212841
    ISBN-13 ‏ : ‎ 978-0387212845
    Item Weight ‏ : ‎ 3.92 pounds
    Dimensions ‏ : ‎ 6.36 x 1 x 9.5 inches


    Differential Equations: An Introduction with Mathematica® (Undergraduate Texts in Mathematics)

    Are you a student or teacher looking to delve into the world of differential equations? Look no further than this comprehensive guide, which combines the theoretical foundations of differential equations with practical applications using the powerful software Mathematica®.

    In this book, you will learn the basics of differential equations, including first-order, second-order, and higher-order equations. You will also explore techniques for solving these equations, such as separation of variables, variation of parameters, and Laplace transforms.

    What sets this book apart is its integration of Mathematica®, a leading software tool for mathematical computation. Through hands-on examples and exercises, you will gain valuable experience in using Mathematica® to solve differential equations and visualize their solutions.

    Whether you are studying differential equations for the first time or looking to deepen your understanding of the subject, this book is an essential resource. Get ready to unlock the power of Mathematica® and master differential equations like never before.
    #Differential #Equations #Introduction #Mathematica #Undergraduate #Texts #Mathematics,machine learning: an applied mathematics introduction

  • Discovering Statistics Using R


    Price: $42.39
    (as of Jan 21,2025 22:36:31 UTC – Details)




    ASIN ‏ : ‎ B00HPZ4VVM
    Publisher ‏ : ‎ SAGE Publications Ltd; 1st edition (March 7, 2012)
    Publication date ‏ : ‎ March 7, 2012
    Language ‏ : ‎ English
    File size ‏ : ‎ 35221 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 992 pages

    Customers say

    Customers find the book provides an engaging introduction to statistics using R. They appreciate the humor and clear explanations that make the concepts easy to understand. The content is described as excellent, a good guide for R, and a good text book to keep even after exams. Readers mention it covers a wide range of scenarios and techniques, including mixed model analysis, multivariate methods, and data distribution. The R code coverage is helpful and detailed, making it a great resource for ramping up on R. Overall, customers consider the book an exceptional value for the breadth of content.

    AI-generated from the text of customer reviews


    Are you interested in statistics and want to learn how to use R to analyze data? Look no further than “Discovering Statistics Using R”! This comprehensive book provides a step-by-step guide to using R for statistical analysis, making it perfect for beginners and experienced users alike.

    With clear explanations, practical examples, and exercises to reinforce your learning, “Discovering Statistics Using R” will help you master essential statistical techniques and gain a deeper understanding of how to interpret and present your data. Whether you are a student, researcher, or data enthusiast, this book is a valuable resource for anyone looking to harness the power of R for statistical analysis.

    So why wait? Dive into the world of statistics with R and unlock the potential of your data with “Discovering Statistics Using R”!
    #Discovering #Statistics,machine learning: an applied mathematics introduction

  • Matlab for Engineers


    Price: $115.99
    (as of Jan 21,2025 21:31:37 UTC – Details)




    Publisher ‏ : ‎ Pearson College Div; 3rd edition (January 1, 2011)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 656 pages
    ISBN-10 ‏ : ‎ 0132103257
    ISBN-13 ‏ : ‎ 978-0132103251
    Item Weight ‏ : ‎ 2.3 pounds
    Dimensions ‏ : ‎ 8 x 0.75 x 10 inches

    Customers say

    Customers find the book easy to use and a good reference for learning programming basics. They appreciate the thorough examples and range of problem topics that cover Matlab’s functionality. The book is described as sturdy and in good condition.

    AI-generated from the text of customer reviews


    Are you an engineer looking to enhance your skills in data analysis, modeling, and simulation? Look no further than Matlab!

    Matlab is a powerful software tool that is widely used in the engineering field for its ability to quickly and efficiently analyze and visualize complex data sets. Whether you are working on signal processing, control systems, image processing, or any other engineering discipline, Matlab can help you streamline your workflow and make your projects more efficient.

    In this post, we will explore some of the key features of Matlab that make it a valuable tool for engineers. From its extensive library of built-in functions to its user-friendly interface, Matlab offers a wide range of capabilities that can help you tackle even the most challenging engineering problems.

    So whether you are a seasoned engineer looking to expand your skill set or a student just starting out in the field, consider adding Matlab to your toolbox. With its versatility and power, Matlab can help you take your engineering projects to the next level.
    #Matlab #Engineers,machine learning: an applied mathematics introduction

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