| 书目名称 | Probability and Statistics for Computer Science | | 编辑 | David Forsyth | | 视频video | | | 概述 | list of terms a student should understand,.list of facts a student should keep in mind,.list of procedures a student should be able to use,.list of practical skills a student should have absorbed..All | | 图书封面 |  | | 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..• Achapter 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 deal | | 出版日期 | Textbook 2018 | | 关键词 | Summarizing 1D data; Boxplots; Datasets; Spatial data; Random variables; Conditional probability; Expected | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-64410-3 | | isbn_softcover | 978-3-319-87788-4 | | isbn_ebook | 978-3-319-64410-3 | | copyright | Springer International Publishing AG 2018 |
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发表于 2025-3-21 16:19:08
