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# Mathematics for Machine Learning

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You save 63% $35.00The 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.

# Machine Learning: An Applied Mathematics Introduction

A **fully self-contained** introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus. Machine Learning: An Applied Mathematics Introduction **covers the essential mathematics behind all of the most important techniques. **

* Chapter list*:

- Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)
- General Matters (In one chapter all of the mathematical concepts you'll need to know. From jargon and notation to maximum likelihood, from information theory and entropy to bias and variance, from cost functions to confusion matrices, and more)
- K Nearest Neighbours
- K Means Clustering
- Naïve Bayes Classifier
- Regression Methods
- Support Vector Machines
- Self-Organizing Maps
- Decision Trees
- Neural Networks
- Reinforcement Learning

An appendix contains links to data used in the book, and more.

The book includes many real-world examples from a variety of fields including

- finance (volatility modelling)
- economics (interest rates, inflation and GDP)
- politics (classifying politicians according to their voting records, and using speeches to determine whether a politician is left or right wing)
- biology (recognising flower varieties, and using heights and weights of adults to determine gender)
- sociology (classifying locations according to crime statistics)
- gambling (fruit machines and Blackjack)
- business (classifying the members of his own website to see who will subscribe to his magazine)

Paul Wilmott brings three decades of experience in education, and his inimitable style, to this, the hottest of subjects. This book is an accessible introduction for anyone who wants to understand the foundations and put the tools into practice.