Beginning BBC microbit A Practical Introduction to microbit Development 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Introduction to Machine Learning with Python 英文版

Title: Introduction to Machine Learning, 3rd Edition Author: Ethem Alpaydin Length: 640 pages Edition: 3rd Language: English Publisher: The MIT Press Publication Date: 2014-08-22 ISBN-10: 0262028182 ISBN-13: 9780262028189 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning method

数学历史的介绍，讲解非常清晰，第七版，可以加深对数学的理解

《计算机系统设计原理》是第一本阐述计算机系统设计中的基本原理和抽象的教材，是麻省理工开放式课程计划(MIT Open Courseware)中“计算机系统工程”课程的主教材。计算机系统的基本原理横跨于操作系统、网络、数据库、分布式系统、程序设计语言、软件工程以及计算机体系结构等方面。通过详细分析每个基本原理的案例，《计算机系统设计原理》演示了如何应用这些原理和抽象来解决实际的计算机系统设计问题。

Introduction to Materials Management 6th Edition CPIM参考资料

统计学是一门十分重要的课程，在本人所了解的所有资料中，本书是评价最高的一本。

An Introduction to Genetic Algorithms Mitchell Melanie A Bradford Book The MIT Press Cambridge, Massachusetts • London, England Fifth printing, 1999

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