Machine Learning: A Bayesian and Optimization Perspective

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(as of Dec 26,2024 12:54:50 UTC – Details)

Publisher ‏ : ‎ Academic Press; 2nd edition (July 31, 2020)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 1160 pages
ISBN-10 ‏ : ‎ 0128188030
ISBN-13 ‏ : ‎ 978-0128188033
Item Weight ‏ : ‎ 5.4 pounds
Dimensions ‏ : ‎ 7.5 x 2.25 x 9.25 inches

Machine Learning: A Bayesian and Optimization Perspective

In the world of machine learning, there are various approaches and techniques that can be used to build predictive models and make sense of complex data. Two key perspectives that are often used in machine learning are Bayesian inference and optimization.

Bayesian inference is a statistical method that uses probability theory to update beliefs about the parameters of a model based on new evidence or data. By treating model parameters as random variables and incorporating prior knowledge, Bayesian inference allows for more robust and interpretable modeling.

On the other hand, optimization techniques are used to find the best set of parameters for a given model by minimizing a loss function. This involves iteratively updating model parameters to improve the model’s performance on a given task, such as classification or regression.

By combining Bayesian inference with optimization techniques, machine learning practitioners can build more accurate and efficient models. Bayesian optimization, for example, is a popular approach that uses Bayesian inference to model the uncertainty in the relationship between model parameters and the objective function, and optimization techniques to find the best set of parameters.

Overall, a Bayesian and optimization perspective in machine learning can lead to more reliable and interpretable models that can make better predictions and decisions. By understanding and incorporating these perspectives into their workflow, machine learning practitioners can unlock the full potential of their data and build more powerful predictive models.
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