Fundamentals of Pattern Recognition and Machine Learning
暫譯: 模式識別與機器學習基礎

Braga-Neto, Ulisses

Fundamentals of Pattern Recognition and Machine Learning

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商品描述

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University.

The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification.

The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.

商品描述(中文翻譯)

《模式識別與機器學習基礎》旨在為研究生或高年級本科生提供一個為期一或兩學期的入門課程。這本書結合了理論與實踐，適合用於課堂教學和自學。它源自於作者在德州農工大學教授這一主題的課程講義和作業，已有13年的歷史。

本書旨在簡潔而全面。它並不試圖採取百科全書式的方式，而是詳細涵蓋了模式識別和機器學習中常用的工具，包括分類、降維、回歸和聚類，以及最近流行的主題，如高斯過程回歸和卷積神經網絡。此外，所選主題在同類文本中具有幾個獨特的特點：它包含了一個關於分類器錯誤估計的廣泛章節，以及關於貝葉斯分類、貝葉斯錯誤估計、獨立抽樣和基於排名的分類的部分。

本書在數學上是嚴謹的，涵蓋了該領域的經典定理。然而，書中努力在理論與實踐之間取得平衡。特別是，書中使用了來自生物信息學和材料信息學應用的數據集來說明理論。這些數據集可從書籍網站獲得，用於基於 Python 和 scikit-learn 的章末編碼作業。文本中的所有圖表均使用 Python 腳本生成，這些腳本也可在書籍網站上獲得。

作者簡介

Ulisses Braga-Neto, Ph.D. is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. His main research areas are pattern recognition, machine learning, statistical signal processing, and applications in bioinformatics and materials informatics. He has worked extensively in the field of error estimation for pattern recognition and machine learning, having received an NSF CAREER award for research in this area, and co-authored a monograph with Edward R. Dougherty on the topic. He has also made contributions to the field of Mathematical morphology in signal and image processing.

作者簡介(中文翻譯)

Ulisses Braga-Neto 博士是德州農工大學電機與計算機工程系的教授。他的主要研究領域包括模式識別、機器學習、統計信號處理，以及在生物資訊學和材料資訊學中的應用。他在模式識別和機器學習的誤差估計領域有廣泛的研究，並因在該領域的研究獲得了 NSF CAREER 獎，並與 Edward R. Dougherty 共同撰寫了一本專著。他還對信號和影像處理中的數學形態學領域做出了貢獻。

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