经典优化理论与应用合集(全局最优化,凸优化,数值优化及优化综述文章)

介绍多目标跟踪算法，包括（1）多目标跟踪处理流程，（2）数据关联算法；（3）联合概率数据互联算法

用LSTM长短期记忆网络实现的金融序列单步预测的代码，基于keras框架搭建的模型，可以用于参考学习

支持向量机演讲的ppt ， 其中牵扯数学问题比较多。 适合于一节课的svm讲解。

id3算法利用matlab的实现，利用treeplot画出生成的决策树

Prism7.2.0.1367 01-BootstrapperShell 02-Regions 03-CustomRegions 04-ViewDiscovery 05-ViewInjection 06-ViewActivationDeactivation 07-Modules - AppConfig 07-Modules - Code 07-Modules - Directory 07-Modules - LoadManual 08-ViewModelLocator 09-ChangeConvention 10-CustomRegistrations 11-UsingDelegateCommands 12-UsingCompositeCommands 13-IActiveAwareCommands 14-UsingEventAggregator 15-FilteringEvents 16-RegionContext 17-BasicRegionNavigation 18-NavigationCallback 19-NavigationParticipation 20-NavigateToExistingViews 21-PassingParameters 22-ConfirmCancelNavigation 23-RegionMemberLifetime 24-NavigationJournal 25-NotificationRequest 26-ConfirmationRequest 27-CustomContent 28-CustomRequest 29-InvokeCommandAction

数字信号处理 丁玉美 西安电子科技大学出版社

一个分析脑电波信号的实用工具中文手册。手册中详尽的脑电波信号处理分析步骤说明有助于初学者分析使用。在MATLAB工具箱中嵌入EEGLAB工具箱即可调用。

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MySQL Workbench汉化xml文件测试可用

大家好，我是Mac Jiang,今天和搭建分享的是台湾大学机器学习基石（Machine Learning Foundations）的个人笔记。个人觉得这门课是一门非常好的机器学习入门课程，值得初学者学习！这份笔记是本人一笔一划手写，扫描后上传了，也算是一个月的心血，希望我的工作能够给大家带来一些学习上的帮助。Week 1: The leaming ProblemDateP1. What is Machine Learning?m:画过观架( observation)款悍技能〔)cb→圆>skdem0过数(d枝能(s)t→DML→5m Prove:增进某种表现 Performance measurePer fornoMeasureML: an alternative rute to butt aomplacoded systems- When human connot prinn the system manually (navigating on Mars)-When human nnot define the solution esaily(speech/vgual recgnition)-When needing raBid deasions that huniang cannot do (high freguency trading?When neeling to be user-orrented in a massive sale(Consumer-targetd mopketig)dataim proved①有在其些日术Pattern RcRRMLerformance机购在完不知邮们度珠meusure的隐藏规剧料灿2.Appliaation of Machine learniO food: data Twitter data Wordsspill tell food Poisoning Cike lines of resturant properlye clothig. data: scale fiqures +client surveysskill: give good fashion recommendation to Clients3 Hosing, data, characteristic of buidings and their energy loadkill:predict energy lad of other buiding closely9 Trans potation: data: Some traffic sign images and meaningsShill= recognize troffic Sions acuratelyO Education: data. Studerts, records onizes ona math tutoring systemskilL: Predict whether a stdert can give a Comet answer to anther question⑥ ntertainment:da: ho w many users have hated some movies象社解料系子荐你统stiu Predict how a user Would rate an uNatPagc3. Com Ponent of Machine lemn输入:x∈x出:y∈Y睛数(9Mrtm)tf:y→丫〔想下的)抛规律台道数据如 raining examples:D=,,()…(xmhyes分sk:9x→y〔学到的程制的孤)辆一M→9Algur+nH( hy Pothesis Set)色色妇的成坏的Pt8,9∈H,从种中最的即9Leaming model= A and He hypothesis set4.Machine learning and other FiMachine (earring, B do值到约练于B数于的设3CMLatMn鸡:eg如 to find property that15e西不哦啥CDM)若立越西的为9R西事无大大区刷若栖与9关,PM可帮助MLArdt9g让电座有很瞰明的表视(下开)CA工)机学展现A工铝能的方法statistics(计利用瓷料爆到推龙,从数学角出发纯计晨钯机罟孑的方法第2讲: Learn to Answera人阳0 n Hypothesis set(假设集)Xxx)「y=(,许答 Wii threshold飞岁=+,讲卷Wx∠thr