├── .gitignore ├── AcademicTrends ├── Task1 论文数据统计.ipynb ├── Task1 论文数据统计.md ├── Task2 论文作者统计.ipynb ├── Task2 论文作者统计.md ├── Task3 论文代码统计.ipynb ├── Task3 论文代码统计.md ├── Task4 论文种类分类.ipynb ├── Task4 论文种类分类.md ├── Task5 作者信息关联.ipynb ├── Task5 作者信息关联.md ├── img │ ├── task1_image1.png │ ├── task1_image2.png │ ├── task1_image3.png │ ├── task1_image4.png │ ├── task2_image1.png │ ├── task2_image2.png │ ├── task3_image1.png │ ├── task3_image2.png │ ├── task5_image1.png │ └── task5_image2.png └── readme.md ├── AnomalyDetection ├── img │ ├── 5k1sASfi9gVpcrt6bCpE8ac-7XGQlEhRydpM7L2EHZM.original.fullsize.png │ ├── APsWDfG-1NDc-z--7wVPsBICFs7Vvwt0v3T7VZwGvxg.original.fullsize.png │ ├── EVHASE85aZeCS43XNaCfsDK6DOJk-WOhXv7KHwo0EoA.original.fullsize.png │ ├── He6GRseNHy5vCD28xNr5O4wIMSBYC_CMmpmjmWTocg8.original.fullsize.png │ ├── N47Ra3UY3ONpe6opMQoM-LjiauCBqRHf9jISb2yk2As.original.fullsize.png │ ├── UWiX5C7kCHx5yX7O9yQm9F1ndg-QgMqS3BAwIWPB40k.original.fullsize-1609839833441.png │ ├── UWiX5C7kCHx5yX7O9yQm9F1ndg-QgMqS3BAwIWPB40k.original.fullsize.png │ ├── cTjP7hnGGCB-h8sfZLCJio9oqUYvs-sdoxpSRtoGCjE.original.fullsize.png │ ├── fOfAz4nhVr7COjlNqCN_AxWiZgVElfaJq6SS8uSjBR4.original.fullsize.png │ ├── image-20210103174850565.png │ ├── image-20210103174904734.png │ ├── image-20210103182254793.png │ ├── image-20210103183909407.png │ ├── image-20210104144520790.png │ ├── image-20210104144603849.png │ ├── image-20210104150718935.png │ ├── image-20210105140222697-1609839336763.png │ ├── image-20210105140222697.png │ ├── image-20210105140242611-1609839375267.png │ ├── image-20210105140242611.png │ ├── image-20210505101703601.png │ ├── image-20210506144705398.png │ ├── output_28_0-1609839842623.png │ ├── output_28_0.png │ ├── output_32_0-1609839844832.png │ ├── output_32_0.png │ ├── v2-bb94bcf07ced88315d0a5de47677200e_720w.png │ ├── xQz5G5vBRHR6PIT6oUEnnPZ1hV5ccszHuX_Fya9R6sQ.original.fullsize.png │ ├── 图4.1距离检测的困境-离群点A-1609839836032.png │ ├── 图4.1距离检测的困境-离群点A.png │ ├── 定义3-邻域半径-1609839837759.png │ ├── 定义3-邻域半径.png │ ├── 局部异常因子公式-1609839840015.png │ └── 局部异常因子公式.png ├── readme.md ├── 一、概述.md ├── 三、线性模型.md ├── 二、基于统计学的方法.md ├── 五、集成方法.md ├── 四、基于邻近度的方法.md └── 附录.md ├── CityManagement └── readme.md ├── ECommerceSearch └── readme.md ├── EnsembleLearning ├── CH2-机器学习基础模型回顾 │ ├── 1.1.png │ ├── 1.10.png │ ├── 1.11.png │ ├── 1.12.png │ ├── 1.13.png │ ├── 1.14.png │ ├── 1.15.png │ ├── 1.16.png │ ├── 1.17.png │ ├── 1.18.png │ ├── 1.19.png │ ├── 1.2.png │ ├── 1.20.png │ ├── 1.21.png │ ├── 1.22.png │ ├── 1.23.png │ ├── 1.24.png │ ├── 1.25.png │ ├── 1.26.png │ ├── 1.27.png │ ├── 1.28.png │ ├── 1.29.png │ ├── 1.3.png │ ├── 1.30.png │ ├── 1.4.png │ ├── 1.5.png │ ├── 1.6.1.png │ ├── 1.6.2.png │ ├── 1.6.png │ ├── 1.7.png │ ├── 1.8.png │ ├── 1.9.1.png │ ├── 1.9.2.png │ └── 第二章：机器学习基础.ipynb ├── CH3-集成学习之投票法与bagging │ ├── CH3（20210412）.ipynb │ ├── DecisionTree.png │ ├── RandomForest.png │ └── Standalone-Models-for-Binary-Classification.png ├── CH4-集成学习之boosting │ ├── 0001.model │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ ├── 9.png │ ├── Boosting2.ipynb │ ├── dump.raw.txt │ ├── 内存使用量.png │ ├── 准确率对比.png │ └── 速度对比.png ├── CH5-集成学习之blending与stacking │ ├── 1.png │ ├── 2.png │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.png │ └── Stacking.ipynb ├── CH6-集成学习之案例分享 │ ├── 集成学习案例分析1 │ │ ├── .ipynb_checkpoints │ │ │ └── 集成学习-案例分析-杨毅远-checkpoint.ipynb │ │ ├── index.xlsx │ │ ├── submision.csv │ │ ├── submit_example.csv │ │ ├── survey.pdf │ │ ├── test.csv │ │ ├── train.csv │ │ └── 集成学习-案例分析1.ipynb │ └── 集成学习案例分析2 │ │ ├── predict.txt │ │ ├── test.txt │ │ ├── train.txt │ │ └── 集成学习-案例分析2.ipynb └── readme.md ├── FinancialRiskControl ├── Task1 赛题理解.md ├── Task2 数据分析.md ├── Task3 特征工程.md ├── Task4 建模调参.md ├── Task5 模型融合.md ├── baseline.md ├── readme.md └── 直播PPT │ ├── 贷款违约预测_建模调参与模型融合.pdf │ ├── 贷款违约预测_数据探索性分析和特征工程.pdf │ └── 贷款违约预测_赛题理解与基线方案.pptx ├── HandsOnDataAnalysis └── readme.md ├── HeartbeatClassification ├── Task1 赛题理解.md ├── Task2 数据分析.md ├── Task3 特征工程.md ├── Task4 模型调参.md ├── Task5 模型融合.md ├── baseline.ipynb ├── baseline.md └── readme.md ├── IntelligentComputing └── 粒子群算法_Matlab.txt ├── IntroductionExperienceAI └── readme.md ├── LinearAlgebra ├── 第01章 线性方程组 │ ├── 1.1 线性方程组的消元法.md │ ├── 1.1 线性方程组的消元法.pdf │ ├── 1.2 方程组与矩阵.md │ ├── 1.2 方程组与矩阵.pdf │ ├── 1.3 矩阵的秩及方程组解的判别.md │ └── 1.3 矩阵的秩及方程组解的判别.pdf ├── 第02章 矩阵及其运算 │ ├── 2.1 矩阵的基本运算.md │ ├── 2.1 矩阵的基本运算.pdf │ ├── 2.2 分块矩阵及运算.md │ ├── 2.2 分块矩阵及运算.pdf │ ├── 2.3 逆矩阵.md │ ├── 2.3 逆矩阵.pdf │ ├── 2.4 方阵的行列式.md │ ├── 2.4 方阵的行列式.pdf │ ├── 2.5 矩阵LU分解.md │ └── 2.5 矩阵LU分解.pdf └── 第03章 向量空间 │ ├── 3.1 基本概念.md │ ├── 3.1 基本概念.pdf │ ├── 3.2 线性相关与线性无关.md │ ├── 3.2 线性相关与线性无关.pdf │ ├── 3.3 向量空间的基与维数，坐标，过渡矩阵.md │ ├── 3.3 向量空间的基与维数，坐标，过渡矩阵.pdf │ ├── 3.4 矩阵的零空间，列空间，线性方程组解的结构.md │ ├── 3.4 矩阵的零空间，列空间，线性方程组解的结构.pdf │ ├── 3.5 向量的内积和正交阵.md │ └── 3.5 向量的内积和正交阵.pdf ├── MachineLearningFundamentals ├── 01 ml_overvirew.md ├── 02 Linear_regression.ipynb ├── 03 Logistic_regression.ipynb ├── 04 Decision_tree.ipynb ├── 05 Cluster_plus.ipynb ├── 06 Bayes_plus.ipynb ├── 07 Knn.ipynb ├── 08 EM.ipynb ├── 09 条件随机场.ipynb ├── 10 Svm.md ├── data │ ├── datingTestSet2.txt │ ├── logistic_x.txt │ └── logistic_y.txt ├── img │ ├── 1.png │ ├── 2.png │ ├── download.png │ ├── download1.png │ ├── download2.png │ ├── 前向后向1.png │ ├── 前向后向10.png │ ├── 前向后向2.png │ ├── 前向后向3.png │ ├── 前向后向4.png │ ├── 前向后向5.png │ ├── 前向后向6.png │ ├── 前向后向7.png │ ├── 前向后向8.png │ ├── 前向后向9.png │ ├── 学习问题1.png │ ├── 学习问题2.png │ ├── 条件随机场.png │ ├── 简化形式1.png │ ├── 简化形式2.png │ ├── 简化形式3.png │ ├── 简化形式4.png │ ├── 简化形式5.png │ ├── 简化形式6.png │ ├── 隐马尔科夫算法.png │ ├── 预测1.png │ ├── 预测10.png │ ├── 预测11.png │ ├── 预测2.png │ ├── 预测3.png │ ├── 预测4.png │ ├── 预测5.png │ ├── 预测6.png │ ├── 预测8.png │ ├── 预测9.png │ └── 马尔可夫过程.png ├── readme.md ├── 奇异值分解（SVD）及其应用.md ├── 机器学习中的特征工程总结.md └── 机器学习算法分类知识总结.md ├── Mathematics └── readme.md ├── MatrixAnalysis └── README.md ├── MemberOperations ├── XX1.png ├── XX2.png ├── courses.ipynb ├── image (1).png ├── image (2).png ├── image (3).png ├── image (4).png ├── image (5).png ├── image (6).png ├── image.png ├── readme.md └── sales.xlsx ├── NeuralNetwork ├── GNN 教程：Graph 基础知识.md ├── GNN教程：GCN介绍.md ├── readme.md ├── 人工神经网络背后的数学原理.md └── 神经网络知识专题总结.md ├── ProbabilityStatistics ├── 01 随机事件与随机变量.md ├── 02 数理统计与描述性统计.md ├── 03 常见分布与假设检验.md ├── 04 方差分析.md ├── figures_ANOVA │ ├── 2-1.png │ ├── 2-2.png │ ├── 2-3.png │ ├── 3-1.png │ ├── 3-2.png │ └── 3-3.png ├── readme.md └── tupian │ ├── 3西格玛原则.png │ ├── binomial.png │ ├── continuousoverall.PNG │ ├── debug.log │ ├── discreteoverall.PNG │ ├── expo.png │ ├── expo2.png │ ├── normal.png │ ├── poisson.png │ ├── poisson2.png │ ├── uniform.png │ ├── uniform2.png │ ├── 下载.jpg │ ├── 中位数公式.png │ ├── 举个例子.jpg │ ├── 举个栗子.png │ ├── 偏态与峰度.png │ ├── 分布函数与密度函数.png │ ├── 分布函数与密度函数的关系.png │ ├── 变异系数公式.png │ ├── 平均数公式.png │ ├── 方差例子.gif │ ├── 标准正态分布.png │ ├── 样本偏度系数公式.png │ ├── 样本峰度系数.png │ ├── 样本方差公式.png │ ├── 样本极差公式.png │ ├── 样本标准差公式.png │ ├── 概率分布函数定义.png │ ├── 正态分布公式.png │ ├── 正态分布图.png │ ├── 正态分布表示.png │ ├── 百分位数.png │ ├── 百分位数图.png │ ├── 百分位数图0.png │ ├── 线性.png │ ├── 线性回归原理图.png │ ├── 自我介绍.png │ ├── 解方程步骤.png │ ├── 逻辑.png │ └── 非线性.png ├── PurchaseAndRedemptionForecast ├── 01 数据探索与分析.ipynb ├── 02 数据探索与分析.ipynb ├── 03 时间序列规则.ipynb ├── 04 时间序列模型-R语言.ipynb ├── 05 特征工程.ipynb ├── 06 建模预测.ipynb └── README.md ├── RentForecast ├── Task1 赛题分析.ipynb ├── Task2 数据清洗.ipynb ├── Task3 特征工程.ipynb ├── Task4 模型选择.ipynb ├── Task5 模型融合.ipynb ├── Task6 竞赛整理.ipynb ├── readme.md └── 数据集 │ ├── test_a.csv │ ├── train_data.csv │ ├── 数据集字段说明.csv │ └── 评分文件 │ └── sub_a_913.csv ├── SecondHandCarPriceForecast ├── Baseline.md ├── Task1 赛题理解.md ├── Task2 数据分析.md ├── Task3 特征工程.md ├── Task4 建模调参 .md ├── Task5 模型融合.md ├── data │ ├── used_car_sample_submit.csv │ ├── used_car_testA_20200313.zip │ ├── used_car_train_20200313.zip │ └── 数据说明.txt └── readme.md ├── TimeSeries ├── ARMA 时间序列模型与预测.md ├── ARMA时间序列用到的数据集 │ ├── population.csv │ ├── readme.md │ └── 人口普查.xlsx ├── R 语言基础.md ├── Time Series Analysis.pdf ├── readme.md ├── typora-user-images │ ├── image-20210922175847365.png │ ├── image-20210922180552465.png │ ├── image-20210922185944045.png │ ├── image-20210922191248078.png │ ├── image-20210923195745948.png │ ├── image-20210923200033331.png │ ├── image-20210924203402681.png │ ├── image-20210924204531153.png │ ├── image-20210924205843412.png │ ├── image-20210924211135207.png │ ├── image-20210924211220048.png │ └── image-20210924211736213.png ├── 手算时间序列.md ├── 时间序列参考网课与教材.md └── 统计与时间序列分析基础.md ├── WeatherOceanForecasts ├── 1.TC AI Earth Baseline.ipynb ├── 1.TC AI Earth Baseline.md ├── 2.Docker提交.ipynb ├── 2.Docker提交.md ├── README.md ├── baseline相关.md ├── code │ └── mlp_predict.py ├── pic │ ├── 1st.jpeg │ ├── 2nd.jpeg │ ├── 3rd.jpeg │ ├── first_step.jpeg │ ├── logo.jpg │ ├── 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