Description: Probabilistic Machine Learning by Kevin P. Murphy Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description "An advanced book for researchers and graduate students working in machine learning and statistics that reflects the influence of deep learning"-- Publisher Description An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning- An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment Author Biography Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling. Details ISBN 0262048434 ISBN-13 9780262048439 Title Probabilistic Machine Learning Author Kevin P. Murphy Format Hardcover Year 2023 Pages 1360 Publisher MIT Press Ltd GE_Item_ID:159242555; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 178.81 USD
Location: Fairfield, Ohio
End Time: 2024-11-13T04:46:01.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
ISBN-13: 9780262048439
Book Title: Probabilistic Machine Learning
Number of Pages: 1360 Pages
Publication Name: Probabilistic Machine Learning : Advanced Topics
Language: English
Publisher: MIT Press
Publication Year: 2023
Item Height: 2.1 in
Subject: Intelligence (Ai) & Semantics, Computer Science, General
Item Weight: 81.3 Oz
Type: Textbook
Author: Kevin P. Murphy
Item Length: 9.3 in
Subject Area: Computers, Science
Item Width: 8.5 in
Series: Adaptive Computation and Machine Learning Ser.
Format: Hardcover