Price: $150.00 - $142.33
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Publisher ‏ : ‎ The MIT Press (August 15, 2023)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 1360 pages
ISBN-10 ‏ : ‎ 0262048434
ISBN-13 ‏ : ‎ 978-0262048439
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 8.38 x 2.18 x 9.31 inches

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)

In the world of machine learning, probabilistic models have gained popularity due to their ability to handle uncertainty and make informed decisions based on probabilities. In this post, we will delve into advanced topics in probabilistic machine learning, focusing on the Adaptive Computation and Machine Learning series.

Probabilistic machine learning is a powerful framework that allows us to model complex relationships in data and make predictions while accounting for uncertainty. The Adaptive Computation and Machine Learning series offers a comprehensive guide to mastering advanced topics in this field, including Bayesian inference, Gaussian processes, and variational inference.

One key concept in probabilistic machine learning is Bayesian inference, which allows us to update our beliefs about a hypothesis as we observe new data. This framework is essential for building flexible and robust models that can adapt to changing environments.

Another important topic covered in the series is Gaussian processes, which are a versatile tool for modeling complex relationships in data. These non-parametric models offer a flexible way to capture dependencies between variables and make predictions with uncertainty estimates.

Lastly, the series explores variational inference, a powerful technique for approximating complex posterior distributions in probabilistic models. By optimizing a lower bound on the true posterior, variational inference allows us to efficiently learn the parameters of our model and make accurate predictions.

Overall, the Adaptive Computation and Machine Learning series provides a deep dive into advanced topics in probabilistic machine learning, offering a wealth of knowledge for researchers and practitioners alike. If you’re interested in mastering probabilistic modeling and making informed decisions based on uncertainty, this series is a must-read.
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