Mathematics for Machine Learning  - Mathematics Behind Machine Learning Algorithms

Mathematics for Machine Learning - Mathematics Behind Machine Learning Algorithms (English, Paperback, Deisenroth Marc Peter)

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Mathematics for Machine Learning - Mathematics Behind Machine Learning Algorithms  (English, Paperback, Deisenroth Marc Peter)

3.7
253 Ratings & 25 Reviews
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    Highlights
    • Language: English
    • Binding: Paperback
    • Publisher: Cambridge University Press
    • Genre: Computers, Computer Science, Mathematics, Tech
    • ISBN: 9781108455145, 9781108455145
    • Edition: 2020, 2020
    • Pages: 398
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  • Description
    The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
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    Specifications
    Translation Details
    Translated to
    • English
    Book Details
    Title
    • Mathematics for Machine Learning
    Imprint
    • Cambridge University Press
    Publication Year
    • 2020 May
    Edition Type
    • First Edition (2020)
    Book Type
    • Educational Purpose Artificial Intelligence Data Science Machine Learning
    Exam
    • All
    Award Year
    • NA
    Table of Contents
    • Introduction Importance of Mathematics in Machine Learning How to Use This Book Linear Algebra Vectors and Matrices Matrix Operations Eigenvalues and Eigenvectors Singular Value Decomposition (SVD) Principal Component Analysis (PCA) Calculus Derivatives and Gradients Optimization: Gradient Descent Multivariable Calculus Partial Derivatives and Chain Rule Jacobian and Hessian Matrices Probability and Statistics Probability Theory Basics Probability Distributions Bayes' Theorem Expectation and Variance Maximum Likelihood Estimation (MLE) Statistical Inference Optimization Convex Optimization Gradient-Based Optimization Methods Constrained Optimization Lagrange Multipliers Linear Models and Regression Linear Regression Regularization Techniques (Lasso, Ridge) Logistic Regression Generalized Linear Models (GLM) Classification and Clustering Support Vector Machines (SVM) K-Nearest Neighbors (KNN) K-Means Clustering Gaussian Mixture Models Neural Networks and Deep Learning Basics of Neural Networks Backpropagation and Gradient Descent Deep Learning Fundamentals Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Information Theory Entropy and Information Gain Kullback-Leibler Divergence Mutual Information Cross-Entropy Loss Function Conclusion Applying Mathematics in Machine Learning Future Directions and Advanced Topics
    Number of Pages
    • 544
    Net Quantity
    • 1
    Product Form
    • Paperback
    Latest Version
    • 2020
    Publisher
    • Cambridge University Press
    Primary Author
    • Marc Peter Deisenroth
    Source ISBN
    • 9781108455145
    Alternative Title
    • Mathematics Behind Machine Learning Algorithms
    Genre
    • Computers, Computer Science, Mathematics, Tech
    Source Type
    • NA
    Tags
    • Mathematics Machine Learning AI Data Science Linear Algebra Calculus Optimization Probability Deep Learning Mathematical Foundations Education Tech Books Artificial Intelligence Statistics Machine Learning Algorithms
    BISAC Regional Theme
    • NA
    ISBN13
    • 9781108455145
    Specific Exam
    • Others
    Book Category
    • Higher Education and Professional Books
    Review Author
    • NA
    BISAC Subject Heading
    • COM094000
    Number of Volumes
    • Single
    Editorial Review
    • The book excels at making complex mathematical concepts—such as linear algebra, calculus, probability, and optimization—accessible and relevant to machine learning practitioners. It begins with essential mathematical tools and gradually progresses to more advanced topics, ensuring that readers are equipped with the knowledge needed to understand and implement machine learning algorithms effectively. One of the key strengths of the book is its clarity and structure. The authors avoid overwhelming readers with overly complex formulas and instead focus on providing intuitive explanations, clear examples, and practical applications. The step-by-step approach helps build foundational knowledge before delving into deeper concepts, which is ideal for both beginners and intermediate learners.
    Book Award Country
    • NA
    Book Subcategory
    • Computing and Information Technology Books
    Book Award Type
    • Commended
    Edition
    • 2020
    ISBN of Books In Set
    • 9781108455145
    Review Date
    • NA
    Intended Audience
    • Young Adult, General
    ISBN10
    • 9781108455145
    Author Award Code
    • Commended
    Language
    • English
    Has E-book Sample
    • No
    Book Award Title
    • NA
    University
    • All
    Print Size
    • Long
    Audio Book Details
    Number of Discs
    • 0
    Contributors
    Author Info
    • Marc Peter Deisenroth is a leading expert in the field of machine learning and artificial intelligence. He is currently a Professor of Machine Learning at University College London (UCL). With a PhD from the University of Cambridge, his research focuses on reinforcement learning, Bayesian methods, and probabilistic modeling. Deisenroth has published extensively in top AI and machine learning journals and conferences, contributing to the development of models that handle uncertainty and optimize decision-making. He co-authored "Mathematics for Machine Learning" to provide a comprehensive yet accessible guide to the mathematical principles that power modern machine learning, aiming to make these complex concepts understandable for both students and professionals.
    Awards Received
    • NA - NA
    Other Contributors
    • Na
    University Books Details
    Stream
    • Diploma, Degree
    Degree/Diploma
    • Degree, Diploma
    Specialization
    • All
    Subject
    • Computer Science, Mathematics, Artificial Intelligence
    Term
    • 3rd semester, 2nd semester, 3rd year
    Additional Features
    Age Group
    • 14 above
    Other Features
    • Clear Visuals and Diagrams Worked Examples Code Examples Mathematical Proofs Real-World Applications Collaborative Learning Advanced Topics Focus on Intuition Supplementary Online Resources References for Further Reading
    Key Features
    • Strong Mathematical Foundation, Practical Machine Learning Application, Clear, Structured Learning, Interdisciplinary Approach, Algorithm Focus, For Students & Professionals
    Dimensions
    Width
    • 18 mm
    Height
    • 252 mm
    Length
    • 177 mm
    Depth
    • 0.3
    Weight
    • 800 gr
    In The Box
    • 1 hardcover book
    Series & Set Details
    Series Name
    • Mathematics of Machine Learning
    School Books Details
    Board
    • CBSE, ICSE, Others
    Standard
    • 12
    Subject
    • Computer Science, Mathematics
    Frequently Bought Together
    Please add at least 1 add-on item to proceed
    Ratings & Reviews
    3.7
    253 Ratings &
    25 Reviews
    • 5
    • 4
    • 3
    • 2
    • 1
    • 133
    • 29
    • 26
    • 13
    • 52
    4

    Wonderful

    Everything about the book is good except for the pages which are getting out.. book should have been strong
    READ MORE

    Amtullah Sana Salfi

    Certified Buyer, Hyderabad

    Jul, 2024

    4
    0
    Report Abuse
    5

    Simply awesome

    This book is amazing for those people who have basic linear algebra knowledge
    Every concept is explained in a good manner & most importantly it's explained in a compact way! All the tools you need to solve a question are there but again if you don't have basic knowledge of linear algebra you'll face issues

    Another thing is this book is totally focused on Linear algebra concepts no programming is included

    My verdict is go for this book if you wanna explore more in Linear algebra which woul...
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    Sayan Bhowmick

    Certified Buyer, Kolkata

    Mar, 2023

    8
    0
    Report Abuse
    3

    Good

    The quaility of page and font is not very good
    READ MORE

    Ashish

    Certified Buyer, Bangalore

    10 months ago

    3
    1
    Report Abuse
    5

    Excellent

    new book
    READ MORE

    Sivasankkar Paramanandam

    Certified Buyer, Erode

    7 months ago

    2
    0
    Report Abuse
    5

    Brilliant

    Nice book for basic maths required for ML.
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    Raunak Srivastava

    Certified Buyer, Kanpur

    Jun, 2023

    3
    0
    Report Abuse
    3

    Good

    This book is not beginner friedly. While reading i found it is easy if i have already know the topic, but if the topic is unknown, it was very hard to understand from this book alone. Consider this book as provider for other resources what are really valuable. This book itself not descrptive in nature, nor provides real life examples clearly. This book is neighter for math nor for ML.
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    Amrit Dutta

    Certified Buyer, Puruliya

    10 months ago

    1
    0
    Report Abuse
    1

    Did not meet expectations

    Received fake product. This is a duplicate product sold by the seller. Dimension are smaller as compared to original book. You can read any graphs as are not visible clearly. Fonts are very small, which causes visibility issue.
    READ MORE

    Shashi Bhushan

    Certified Buyer, Gurugram

    4 months ago

    5
    0
    Report Abuse
    5

    Super!

    It was excellent. The quality of the book is very good.....fully satisfied with
    READ MORE

    Snehasis Kundu

    Certified Buyer, Jamshedpur

    6 months ago

    0
    0
    Report Abuse
    5

    Must buy!

    This book is suitable for intermediate learners of AI, Data Science, CSE, Mathematics and Statistics. Plus if you are preparing for GATE-DA and entrance exams/interviews to get into department of AI-ML-DS, the content this book has is going to be indispensable.
    READ MORE

    Abhrodeep Das

    Certified Buyer, Siliguri

    10 months ago

    1
    0
    Report Abuse
    1

    Very poor

    Front size is too small
    READ MORE

    Flipkart Customer

    Certified Buyer, Ghaziabad

    3 days ago

    0
    0
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    All 25 reviews
    Questions and Answers
    Q:Is the paperback version of this book in color print or grayscale ?
    A:Hardcover black white
    ExamTime2023
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    Q:Is it a coloured print book or grayscale and how's the page quality?.
    A:Grayscale edition
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    Q:It's quality of page and book is good or not
    A:Yeah.. the page quality is ok.. but most of the topics required for MTech are missing..
    KARAN BHATIA
    Certified Buyer
    2
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    Q:Is the book colorful like in pdf
    A:No
    BOOKSHEAVEN18
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    Q:How is the page print quality? Indian versions tend to use a thinner page which sometime makes diagrams hard to read
    A:Don't worry. Paper quality is good and u read diagrams easily
    Pustakmarket
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    Q:Is this book from Cambridge University 2020 ???
    A:Yes
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    Q:Is this a colored (or) greyscale version?
    A:Greyscale
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    Q:Is this first edition or latest edition?
    A:Latest edition
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    Q:HI, is the paperback version color printed? Thanks
    A:Grayscale edition
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