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Mathematics of Machine Learning

You're reading from   Mathematics of Machine Learning Master linear algebra, calculus, and probability for machine learning

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Product type Paperback
Published in May 2025
Publisher Packt
ISBN-13 9781837027873
Length 730 pages
Edition 1st Edition
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Author (1):
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Tivadar Danka Tivadar Danka
Author Profile Icon Tivadar Danka
Tivadar Danka
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Toc

Table of Contents (36) Chapters Close

Introduction Part 1: Linear Algebra FREE CHAPTER
1 Vectors and Vector Spaces 2 The Geometric Structure of Vector Spaces 3 Linear Algebra in Practice 4 Linear Transformations 5 Matrices and Equations 6 Eigenvalues and Eigenvectors 7 Matrix Factorizations 8 Matrices and Graphs References
Part 2: Calculus
9 Functions 10 Numbers, Sequences, and Series 11 Topology, Limits, and Continuity 12 Differentiation 13 Optimization 14 Integration References
Part 3: Multivariable Calculus
15 Multivariable Functions 16 Derivatives and Gradients 17 Optimization in Multiple Variables References
Part 4: Probability Theory
18 What is Probability? 19 Random Variables and Distributions 20 The Expected Value References
Part 5: Appendix
Other Books You May Enjoy
Index
Appendix A It’s Just Logic 1. Appendix B The Structure of Mathematics 2. Appendix C Basics of Set Theory 3. Appendix D Complex Numbers

14
Integration

When we first encountered the concept of derivatives in Chapter 12, we introduced it through an example from physics. As Newton created it, the derivative describes the velocity of a moving object as calculated from its time-distance graph. In other words, the velocity can be derived from the time-distance information.

Can the distance be reconstructed given the velocity? In a sense, this is the inverse of differentiation.

Questions such as these are hard to answer if we only look at the most general case, so let’s consider a special one. Suppose that our object is moving with a constant velocity v(t) = v0ms-, for a duration of T seconds. With some elementary logic, we can conclude that the total distance traveled is v0T meters.

When taking a look at the time-velocity plot, we can immediately see that the distance is the area under the time-velocity function graph v(t) = v0.

The graph of v(t) describes a rectangle with width v0 and length T, hence its area is indeed...

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