#9931

Mentioned in 5 episodes

Pattern Recognition and Machine Learning

Book • 2006

Christopher M. Bishop

This book offers a detailed introduction to pattern recognition and machine learning, integrating both fields under a common statistical framework.

It covers topics such as Bayesian methods, graphical models, kernel-based algorithms, and neural networks, making it suitable for advanced undergraduates, first-year PhD students, researchers, and practitioners.

The book includes a wide range of exercises and is supported by additional materials like lecture slides and figures.

Mentioned by

Andrew Lawrence

Mentioned in 5 episodes

Mentioned by

Minqi Jiang

as a resource for self-study in machine learning.

#114 - Secrets of Deep Reinforcement Learning (Minqi Jiang)

Mentioned by

Bert de Vries

as a book that taught him machine learning basics.

Prof. BERT DE VRIES - ON ACTIVE INFERENCE

Mentioned by

Yannic Kilcher

when discussing the PRML book and model-based machine learning.

ICLR 2020: Yann LeCun and Energy-Based Models

Authored by

Chris Bishop

, serving as an essential reference for machine learning students and researchers.

Prof. Chris Bishop's NEW Deep Learning Textbook!

Mentioned by

Andrew Lawrence

as a good fundamental book for machine learning.

Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023)

Mentioned by

Sayak Paul

as a book recommended during his undergraduate course on Pattern Recognition and Machine Learning.

Mentioned by

Daniel Wilson

as a resource for learning machine learning.

Mapping the intersection of AI and GIS

Referenced to show how theoretical arguments can be wrong in machine learning, using an example from polynomial regression.

"IABIED Book Review: Core Arguments and Counterarguments" by Stephen McAleese

Mentioned by

Tim Scarfe

when preparing for the episode, referencing his forgotten knowledge of kernels.

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