Probability and Statistics for Computer Science
暫譯: 計算機科學的機率與統計

Forsyth, David

Probability and Statistics for Computer Science

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商品描述

This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.

With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:

- A treatment of random variables and expectations dealing primarily with the discrete case.

- A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.

- A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.

- A chapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.

- A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.

- A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis.

- A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.

Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as

boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know.

Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

商品描述(中文翻譯)

這本教科書針對計算機科學的本科生，適合大二下學期或大三上學期的學生，提供質性和量性數據分析、機率、隨機變數及統計方法（包括機器學習）的全面背景。

《計算機科學的機率與統計》仔細處理了滿足課程需求的主題，特點包括：

- 對隨機變數和期望的處理，主要針對離散情況。
- 實用的模擬處理，展示如何提取許多有趣的機率和期望，特別強調馬可夫鏈。
- 對簡單點推斷策略（最大似然估計；貝葉斯推斷）在簡單情境中的清晰而簡潔的說明。這部分擴展到涵蓋一些信賴區間、隨機抽樣的樣本和母體，以及最簡單的假設檢定。

- 一章處理分類，解釋其用途；如何使用隨機梯度下降訓練支持向量機（SVM）分類器；以及如何使用更先進方法的實現，如隨機森林和最近鄰居。

- 一章處理回歸，解釋如何在實際問題中設置、使用和理解線性回歸和最近鄰居回歸。

- 一章處理主成分分析，仔細發展直覺，並包含許多實際範例。還簡要描述了通過主坐標分析進行的多變量縮放。

- 一章處理通過聚合方法和k-means進行的聚類，展示如何為複雜信號構建向量量化特徵。

每個主要章節都配有插圖，包含許多實例和其他教學元素，如框架程序、定義、有用事實和記住這些（簡短提示）。每章末尾有問題和程式設計練習，並總結讀者應該了解的內容。

教師資源包括所有問題的完整模型解答集，以及附有演示幻燈片的教師手冊。

作者簡介

David Alexander ​Forsyth is Fulton Watson Copp Chair in Computer Science at the University of Illinois at Urbana-Champaign, where he is a leading researcher in computer vision.
Professor Forsyth has regularly served as a program or general chair for the top conferences in computer vision, and has just finished a second term as Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Fellow of the ACM (2014) and IEEE (2009), Forsyth has also been recognized with the IEEE Computer Society's Technical Achievement Award (2005), the Marr Prize, and a prize for best paper in cognitive computer vision (ECCV 2002). Many of his former students are famous in their own right as academics or industry leaders.

He is the co-author with Jean Ponce of Computer Vision: A Modern Approach (2002; 2011), published in four languages, and a leading textbook on the topic.

Among a variety of odd hobbies, he is

a compulsive diver, certified up to normoxic trimix level.

作者簡介(中文翻譯)

大衛·亞歷山大·福賽斯（David Alexander Forsyth）是伊利諾伊大學香檳分校（University of Illinois at Urbana-Champaign）計算機科學的富爾頓·沃森·科普（Fulton Watson Copp）講座教授，他是計算機視覺領域的領先研究者。福賽斯教授曾多次擔任計算機視覺頂級會議的程序或總主席，並剛剛結束了他在《IEEE模式分析與機器智能期刊》（IEEE Transactions on Pattern Analysis and Machine Intelligence）擔任主編的第二個任期。

福賽斯是ACM（2014年）和IEEE（2009年）的會士，並曾獲得IEEE計算機學會的技術成就獎（2005年）、馬爾獎（Marr Prize）以及在認知計算機視覺領域的最佳論文獎（ECCV 2002）。他的許多前學生在學術界或業界都是著名的領導者。

他與讓·龐斯（Jean Ponce）共同撰寫的《計算機視覺：現代方法》（Computer Vision: A Modern Approach，2002年；2011年）已出版四種語言，是該主題的主要教科書。

在各種奇特的愛好中，他是一名強迫症潛水者，持有至常氧三混合氣（normoxic trimix）級別的潛水證照。

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