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Introduction to Machine Learning with R: Rigorous Mathematical Analysis
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(as of Dec 25,2024 00:11:13 UTC – Details)
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Publisher : O’Reilly Media; 1st edition (May 1, 2018)
Language : English
Paperback : 223 pages
ISBN-10 : 1491976446
ISBN-13 : 978-1491976449
Item Weight : 12.8 ounces
Dimensions : 7 x 0.4 x 9.1 inches
Customers say
Customers find the book provides a good introduction to machine learning concepts like regression, decision trees, and neural networks. They say it’s simple and comprehensive, giving them a decent understanding of the basics. However, some readers report issues with typos and incorrect text.
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Machine learning is a powerful tool that allows computers to learn patterns and make predictions based on data. In this post, we will provide an introduction to machine learning with R, focusing on the rigorous mathematical analysis that underpins this field.
Machine learning involves building models that can learn from data and make predictions or decisions without being explicitly programmed. The goal is to develop algorithms that can generalize well to new, unseen data.
R is a popular programming language and environment for statistical computing and graphics. It has a wide range of libraries and packages that make it well-suited for machine learning tasks.
To understand machine learning with R, it is important to have a solid foundation in mathematics. Some key mathematical concepts that are essential for machine learning include:
1. Linear algebra: Linear algebra is essential for understanding how machine learning models work. It involves operations on vectors and matrices, which are used to represent data and parameters in machine learning algorithms.
2. Calculus: Calculus is used to optimize machine learning models by finding the minimum or maximum of a function. Techniques such as gradient descent are used to update model parameters to minimize a loss function.
3. Probability and statistics: Probability theory and statistics are used to model uncertainty in data and make predictions. Concepts such as probability distributions, hypothesis testing, and Bayesian inference are commonly used in machine learning.
4. Optimization: Optimization is a key component of machine learning, as models are trained by finding the best set of parameters that minimize a loss function. Techniques such as gradient descent and stochastic gradient descent are used to optimize model parameters.
By understanding these mathematical concepts and applying them in R, you can build and analyze machine learning models with confidence. Stay tuned for more posts on specific machine learning algorithms and techniques in R.
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