weixin_44495738的博客

INF 442 Amphi 7: Linear models for classificationIntroductionIntroductionPrediction Error: L:Y×Y→RL: \mathcal{Y} \times \mathcal{Y} \to \mathbb{R}L:Y×Y→R目的: minE(X,Y)L(Y,f(X))min \mathbb{E}_{(X,Y)}L(Y,f(X))minE(X,Y)​L(Y,f(X))实际上：min1n∑iL(Yi,f(Xi))min \

INF442 Amphi 7:Linear models for regression | Standard Template Libraryk-NN的优缺点1. Linear model for regressionOrdinary Least Squares(OLS) estimatorOptimalityEvaluationDegenerate CasesSol: RegularizationRidge Regression2. Non-Linear regression2.1 Using basis

INF442 Amphi 6: Supervised learning and k-NN predictors | Genericity1. Supervised Learning1.1 不同的损失函数1.2 Regression with squared error1.3 Classification with 0-1 loss1.4 优缺点1.5 怎么选取k值？1.5.1 Cross-Validation1.5.2 Evaluate a classifier's performance2. Gener

INF442 Amphi 5: Density estimation｜Inheritance1. Density Estimation1.1 Quality of a density estimator1.2 Parametric | Non-parametric Estimation1.3 Parametric Method1.3.1 Gaussian Model1.3.2 Mixture Models1.4 Non-parametric Method1.4.1 Histograms1.4.2 Kerne

INF442 Amphi 4: Hierarchical Clustering | Classes 2/21. Hierarchical Clustering1.0 Dendrogram1.1 构建Hierarchy的两种方法：AHC,DHC1.2 AHC伪代码1.3 Cluster间距离的定义1.4 Single-Linkage的AHC1.5 Ultrametric1.6 Single-Linkage缺点：Chaining Effect1.7 Phylogenetic trees1.8 UPGMA Alg

INF Amphi 3: Clustering | Classes1. Clustering1.0 简介1.1 K-Means简介1.2 如何选取每一个类别的中心点1.3 如何在给定每一个类别的中心点下划分每一个样本点的归属1.4 关于Minimum Local的证明1.5 如何计算一个Partition1.6 Lloyd's Variational Heuristic / K-Means1.7 初始化方法1.8 K-Means ++1.9 如何选取合适的k的值 ｜ Elbow ｜Silhouette2.

INF442 Amphi 2: Algorithmes pour l'analyse de données en C++1. Nearest Neighbor Search1.1 实现方法一：Linear scan1.2 数据预处理｜kd-tree1.2.1 构建过程 ｜ Cyclic iteration1.2.2 kd树对于NN Search的实现2. C++ as C (2/2)2.1 常见错误2.1.1 Pointers to unitialized data2.1.2 Dangling pointe

UNIX/Linux terminal 命令和Makefileecho,cat,>,>>,<MakefileManUID (User Identifier), GID (Group Identifier)Environment Variablesls和对应的权限chmod 修改权限File SearchProcessUNIX SignalsGrepSequential and Parallel Execitionecho,cat,>,>>,<Streams

INF442 Amphi 1:Introduction / C++ as C0. Introduction to Data Science表示方法1：Vector表示方法2：MetricLearning Paradigms2. C++ as C2.1 Hello world2.1.2 编译方法一：2.1.2 编译方法二：makefile2.2 Built-in types2.3 ????Enumerations2.4 Arrays2.5 Standard Input and Output2.6 Switch