Algorithmic Mathematics in Machine Learning

Price: $64.00
(as of Dec 27,2024 10:15:18 UTC – Details)

Publisher ‏ : ‎ SIAM – Society for Industrial and Applied Mathematics (April 8, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 237 pages
ISBN-10 ‏ : ‎ 1611977878
ISBN-13 ‏ : ‎ 978-1611977875
Item Weight ‏ : ‎ 8.3 ounces
Dimensions ‏ : ‎ 10 x 7 x 0.8 inches

Algorithmic Mathematics in Machine Learning

Algorithmic mathematics plays a crucial role in the field of machine learning, as it forms the foundation for the development of various machine learning models and algorithms. Machine learning algorithms are essentially mathematical computations that enable machines to learn from data and make predictions or decisions without being explicitly programmed.

One of the key aspects of algorithmic mathematics in machine learning is the optimization of mathematical functions. Machine learning models often involve the optimization of complex mathematical functions to minimize errors or maximize predictive accuracy. This optimization process requires a deep understanding of mathematical algorithms such as gradient descent, which is commonly used to iteratively update the parameters of a model to minimize a loss function.

Another important aspect of algorithmic mathematics in machine learning is the use of probabilistic models. Probability theory plays a crucial role in machine learning, as many machine learning algorithms are based on probabilistic models that enable the quantification of uncertainty and the estimation of probabilities. Algorithms such as Bayesian inference and Markov chains are commonly used in machine learning to model complex relationships and make predictions based on probability distributions.

In addition, algorithmic mathematics is essential for the development of machine learning algorithms that can handle large-scale and high-dimensional data. Algorithms such as support vector machines, random forests, and neural networks rely on mathematical principles to efficiently process and analyze massive datasets, enabling machines to learn complex patterns and make accurate predictions.

Overall, algorithmic mathematics is at the core of machine learning, driving the development of innovative algorithms and models that enable machines to learn from data and make intelligent decisions. By leveraging the power of mathematical algorithms, machine learning continues to push the boundaries of what machines can achieve in various applications, from image recognition and natural language processing to autonomous driving and healthcare.
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