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人工智能（哈工大）-赵铁军-2009 ppt
8个部分共9章，覆盖了人工智能研究的核心内容8个部分9章是：人工智能概述—第1章 第1部分搜索(问题求解)—第2章 第2部分逻辑与推理—第3章 第3部分知识表示—第4章 不确定性推理—第5章 第4部分学习—第6章 第5部分自然语言理解简介—第7章 第6部分规划简介—第8章 第7部分多Agent系统—第9章 第8部分
- 2020-11-29下载
- 积分：1
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信号能量计算
计算振动噪声信号能量的程序，提供了一个很好的示例。希望对大家能有帮助。
- 2020-12-01下载
- 积分：1
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车道偏离预警系统
安全辅助驾驶(Safety Driving Assist，简称SDA)是当前国际智能交通系统研究的重要内容， 它主要解决交通安全的问题，对于困扰运输领域的交通堵塞及环境污染两个问题也有缓解作用。基于此，世界上很多国家都在加强车辆安全辅助驾驶技术领域的研究。关于安全辅助驾驶技术的研究主要集中在车道偏离预警，前方障碍物探测，以及驾驶员状态监测等方面。近20年来，车道偏离预警系统作为安全辅助驾驶研究领域的一个组成部分，已经受到越来越多的关注，很多国家都投入大量的人力、物力和财力进行系统研发。 车道偏离预警系统研究背景 根据(美国)联邦公路局的估计，美国2002年所有致命的交通事故中44%
- 2020-12-04下载
- 积分：1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138
- 2020-12-10下载
- 积分：1
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凸优化Matlab工具包.zip
【实例简介】斯坦福的《凸优化》课程配套的求解凸优化问题的Matlab工具包。
- 2021-11-25 00:43:56下载
- 积分：1
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数据挖掘---决策树
数据挖掘作业，决策树，安书上做的 ，有自己的写的图文并茂的word文档
- 2020-11-30下载
- 积分：1
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stm32f334BUCK，逆变程序
使用stm32f334单片机HRTIMER产生BUCK电源电路的PWM和用于全桥逆变的SPWM波。
- 2020-12-11下载
- 积分：1
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2007年全国大学生数学建模竞赛A题.zip
2007年全国大学生数学建模竞赛A题.zip
- 2020-12-08下载
- 积分：1
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stereoMatch
自己整理了下一些立体匹配的算法，包括局部匹配的SAD，NCC，还有简单的NP，也有基于OPENCV的立体匹配，最后一个是基于ELAS的立体匹配算法
- 2020-12-06下载
- 积分：1
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C#毕业设计(全套论文)+源码
【实例简介】C#毕业设计(全套论文)+源码
- 2021-11-06 00:33:16下载
- 积分：1
