Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Probabilistic Machine Learn-Ing from Scratch: Bayesian Methods, Graphical Models & Inference

Probabilistic Machine Learn-Ing from Scratch: Bayesian Methods, Graphical Models & Inference

Hossain, Mir

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798197241160
Publisher: Independently Published
Pages: 276
Weight: 0.82
Height: 0.58 Width: 6.00 Depth: 9.00
Language: English

What if machine learning models could explain uncertainty instead of hiding it?

Most modern machine learning books teach optimization first: define a loss, compute gradients, and train models. But probabilistic machine learning approaches the problem differently. It asks:

What should we believe, and how should those beliefs change when new data arrives?

PROBABILISTIC MACHINE LEARNING FROM SCRATCH is a rigorous, implementation-driven guide to Bayesian methods, graphical models, probabilistic inference, and modern uncertainty-aware AI systems. Designed for serious learners, graduate students, ML engineers, and researchers, this book builds the field from first principles with complete derivations and practical code implementations.

Inside this book, you will learn:

Bayesian probability and statistical inference

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