Open Daily: 10am - 10pm | Alley door 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

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Probabilistic Models and Machine Learning

Probabilistic Models and Machine Learning

Blei, David M.

Hardcover

General Computers

PREORDER - Expected ship date March 31, 2027

ISBN10: 100969362X
ISBN13: 9781009693622
Publisher: Cambridge University Press
Published: Mar 31 2027
Pages: 261
Language: English
This is a concise introduction to probabilistic modeling and machine learning that shows readers how to design models for real data, how to compute with them, and how to evaluate and revise them. The book begins with conjugate models and regression, then moves through mixture models, topic models, matrix factorization, exponential families, hierarchical models, and deep probabilistic models. In parallel, it develops the main computational tools: MAP estimation with stochastic optimization, Gibbs sampling, coordinate-ascent variational inference, stochastic variational inference, and black box variational inference. The result is a coherent treatment bridging Bayesian statistics, probabilistic modeling, machine learning, and connecting to modern artificial intelligence through deep generative models, diffusion models, and large language models. It is an ideal resource for graduate students, practitioners, and researchers.

Also in

General Computers

View All