七月在线机器学习
├─ML_3月机器学习在线班
│&nBSp;&nBSp;├─material
│&nBSp;&nBSp;│&nBSp;&nBSp;├─4月19日晚的分享_黄高乐
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1.1微积分与概率论.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1.微积分与概率论.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10.1贝叶斯网络.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11.支持向量机.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12.EM和GMM.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13.0主题模型_预习材料.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13.主题模型.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14.隐马尔科夫模型.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2.1.1参数估计的评价准则.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2.1参数估计与矩阵运算.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2.参数估计与矩阵运算.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2012.李航.统计学习方法.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3.凸优化.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4.1广义线性回归和对偶优化.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5.梯度下降和拟牛顿.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6.最大熵模型.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7.聚类.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8.决策树与随机森林.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9.ADaboost导论.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9.贝叶斯网络.PPT
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈ADaboost.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈ADaboost.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈book11APril2014.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈CART.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Finding scientific TOPics.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈kernel.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈lda.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈mcmc.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈七月教育LDA学员分享_version2.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈凸优化-中译本(扫描).pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈推荐系统实践.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈学习率代码.CPP
│&nBSp;&nBSp;└─video
│&nBSp;&nBSp;└─├─01 微积分与概率论基础
│&nBSp;&nBSp;└─├─02 参数估计与矩阵运算基础
│&nBSp;&nBSp;└─├─03 凸优化基础
│&nBSp;&nBSp;└─├─04&nBSp;&nBSp;广义线性回归和对偶优化
│&nBSp;&nBSp;└─├─05 牛顿、拟牛顿、梯度下降、随机梯度下降(SGD)
│&nBSp;&nBSp;└─├─06 熵、最大熵模型MaxEnt、改进的迭代尺度法IIS
│&nBSp;&nBSp;└─├─07 聚类（k-means、层次聚类、谱聚类等）
│&nBSp;&nBSp;└─├─08 K近邻、决策树、随机森林(random decision forests)
│&nBSp;&nBSp;└─├─09 ADaboost
│&nBSp;&nBSp;└─├─10 朴素贝叶斯、与贝叶斯网络
│&nBSp;&nBSp;└─├─11 支持向量机（最大间隔分类、拉格朗日乘值、对偶问题、损失函数、最优化理论、smO）
│&nBSp;&nBSp;└─├─12 EM、混合高斯模型
│&nBSp;&nBSp;└─├─12 衣服推荐系统
│&nBSp;&nBSp;└─├─13 主题模型（概率潜语义分析PLSA、隐含狄利克雷分布LDA）
│&nBSp;&nBSp;└─├─14.15 马尔科夫链、隐马尔可夫模型HMM、采样
│&nBSp;&nBSp;└─├─16 马尔可夫随机场(Markov Random Field)、条件随机场CRF
│&nBSp;&nBSp;└─├─17 SVD、主成分分析PCA、因子分析、独立成分分析ICA
│&nBSp;&nBSp;└─├─18 卷积神经网络(CNN)、深度学习浅析
│&nBSp;&nBSp;└─├─19 变分推断方法
│&nBSp;&nBSp;└─└─20 知识图谱
├─ML_9月机器学习在线班
│&nBSp;&nBSp;├─8_9_随机森林_SVM
│&nBSp;&nBSp;│&nBSp;&nBSp;├─css
│&nBSp;&nBSp;│&nBSp;&nBSp;├─data
│&nBSp;&nBSp;│&nBSp;&nBSp;├─IMages
│&nBSp;&nBSp;│&nBSp;&nBSp;├─js
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Practice_logistic.html
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Practice_rf.html
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Practice_svm.html
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈rf.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈svm.pdf
│&nBSp;&nBSp;├─回归代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈d8.txt
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈Regression.py
│&nBSp;&nBSp;├─基础补习-概率-台湾大学叶柄成
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第八周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第二周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第九周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第六周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第七周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第三周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第四周
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第五周
│&nBSp;&nBSp;│&nBSp;&nBSp;└─课堂讲义
│&nBSp;&nBSp;├─课程PPT
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1.1微积分与概率论.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1.微积分与概率论原.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10.降维.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11.聚类.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12.提升.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13.贝叶斯网络.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14.EM.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15.主题模型.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16.采样_更新.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17.HMM.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18.条件随机场.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈19_20_神经网络.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2.1数理统计与参数估计.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3.1矩阵运算.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4.凸优化.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5.1回归.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6.1梯度下降和拟牛顿.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7.1最大熵模型.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8.1rf.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9.1svm.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈cs229-notes1.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈探秘2016校招笔试面试.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈凸优化_CN.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈凸优化_EN.pdf
│&nBSp;&nBSp;├┈0.烟雨蒙蒙.mp4
│&nBSp;&nBSp;├┈1.微积分和概率论.mp4
│&nBSp;&nBSp;├┈10.降维.mp4
│&nBSp;&nBSp;├┈11.聚类.mp4
│&nBSp;&nBSp;├┈12.Boosting.mp4
│&nBSp;&nBSp;├┈13.贝叶斯网络.mp4
│&nBSp;&nBSp;├┈14.EM算法.mp4
│&nBSp;&nBSp;├┈14.EM算法重制完整版.mp4
│&nBSp;&nBSp;├┈15.主题模型.mp4
│&nBSp;&nBSp;├┈16.采样.mp4
│&nBSp;&nBSp;├┈17.HMM.mp4
│&nBSp;&nBSp;├┈18.条件随机场.mp4
│&nBSp;&nBSp;├┈19.人工神经网络.mp4
│&nBSp;&nBSp;├┈2.数理统计与参数估计.mp4
│&nBSp;&nBSp;├┈20.CNN&AMp;RNN.mp4
│&nBSp;&nBSp;├┈3.矩阵运算.mp4
│&nBSp;&nBSp;├┈4.凸优化.mp4
│&nBSp;&nBSp;├┈5.回归.mp4
│&nBSp;&nBSp;├┈6.梯度下降和拟牛顿.mp4
│&nBSp;&nBSp;├┈7.最大熵模型.mp4
│&nBSp;&nBSp;├┈8.随机森林.mp4
│&nBSp;&nBSp;└┈9.支持向量机.mp4
├─ML_机器学习其他资料
│&nBSp;&nBSp;├─2014斯坦福大学机器学习mkv视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├─pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├─PPT
│&nBSp;&nBSp;│&nBSp;&nBSp;├─机器学习课程2014源代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├─教程和笔记
│&nBSp;&nBSp;│&nBSp;&nBSp;├─推荐播放器
│&nBSp;&nBSp;│&nBSp;&nBSp;├─网易视频教程
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 1 - Welcome (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 2 - What is Machine Learning_ (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 3 - Supervised Learning (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 4 - Unsupervised Learning (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 1 - Deciding What to Try NEXT (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 2 - Evaluating a Hypothesis (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 3 - Model Selection and TrAIn_Validation_Test Sets (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 5 - Regularization and Bias_Variance (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 6 - Learning Curves (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 7 - Deciding What to Do NEXT Revisited (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 1 - Prioritizing What to Work On (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 2 - Error Analysis (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 4 - TrADing Off Precision and Recall (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 5 - Data For Machine Learning (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 1 - OptIMization Objective (15 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 2 - Large Margin Intuition (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 4 - Kernels I (16 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 5 - Kernels II (16 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 6 - Using An SVM (21 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 2 - K-Means AlGorithm (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 3 - OptIMization Objective (7 min)(1).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 3 - OptIMization Objective (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 4 - Random Initialization (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 5 - Choosing the Number of Clusters (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 1 - Motivation I_ Data ComPression (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 2 - Motivation II_ Visualization (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 3 - PrincIPal Component Analysis Problem Formulation (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 4 - PrincIPal Component Analysis AlGorithm (15 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 5 - Choosing the Number of PrincIPal Components (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 6 - Reconstruction from ComPressed RePresentation (4 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 7 - ADvice for APPlying PCA (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 1 - Problem Motivation (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 2 - Gaussian Distribution (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 3 - AlGorithm (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 6 - Choosing What Features to Use (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 1 - Problem Formulation (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 2 - Content Based Recommendations (15 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 3 - Collaborative Filtering (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 4 - Collaborative Filtering AlGorithm (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 6 - IMplementational DetAIl_ Mean Normalization (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 - 1 - Learning With Large Datasets (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 - 2 - Stochastic GrADient Descent (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 - 3 - Mini-Batch GrADient Descent (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 - 4 - Stochastic GrADient Descent Convergence (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 - 5 - Online Learning (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18 - 1 - Problem DescrIPtion and PIPeline (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18 - 2 - Sliding Windows (15 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18 - 4 - Ceiling Analysis_ What Part of the PIPeline to Work on NEXT (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈19 - 1 - Summary and Thank You (5 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 1 - Model RePresentation (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 2 - Cost Function (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 3 - Cost Function - Intuition I (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 4 - Cost Function - Intuition II (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 5 - GrADient Descent (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 6 - GrADient Descent Intuition (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 7 - GrADientDescentForLinearRegression&nBSp;&nBSp;(6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 8 - What_s NEXT (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 1 - Matrices and Vectors (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 2 - ADdition and Scalar MultIPlication (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 3 - Matrix Vector MultIPlication (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 4 - Matrix Matrix MultIPlication (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 5 - Matrix MultIPlication Properties (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 6 - Inverse and Transpose (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 1 - MultIPle Features (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 2 - GrADient Descent for MultIPle Variables (5 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 3 - GrADient Descent in Practice I - Feature Scaling (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 4 - GrADient Descent in Practice II - Learning Rate (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 5 - Features and Polynomial Regression (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 6 - Normal Equation (16 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 1 - Basic Operations (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 2 - Moving Data Around (16 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 3 - Computing on Data (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 4 - Plotting Data (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 5 - Control Statements_ for, while, if statements (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 6 - Vectorization (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 7 - Working on and SuBMitting ProgrAMming Exercises (4 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 1 - Classification (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 2 - Hypothesis RePresentation (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 3 - Decision Boundary (15 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 4 - Cost Function (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 5 - SIMplified Cost Function and GrADient Descent (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 6 - ADvanced OptIMization (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 1 - The Problem of Overfitting (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 2 - Cost Function (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 3 - Regularized Linear Regression (11 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 4 - Regularized Logistic Regression (9 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 1 - Non-linear Hypotheses (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 2 - Neurons and the BrAIn (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 3 - Model RePresentation I (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 4 - Model RePresentation II (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 5 - ExAMples and Intuitions I (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 6 - ExAMples and Intuitions II (10 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 7 - Multiclass Classification (4 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 1 - Cost Function (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 2 - BackPropagation AlGorithm (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 3 - BackPropagation Intuition (13 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 4 - IMplementation Note_ Unrolling ParAMeters (8 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 5 - GrADient Checking (12 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 6 - Random Initialization (7 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 7 - Putting It Together (14 min).mkv
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈9 - 8 - Autonomous Driving (7 min).mkv
│&nBSp;&nBSp;├─机器学习导论_42_上海交大(张志华)
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 基本概念.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 核定义.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 正定核性质.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 正定核应用.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 核主元分析.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 主元分析.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 主坐标分析.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 期望最大算法.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 概率PCA.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18 最大似然估计方法.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈19 EM算法收敛性.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 随机向量.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈20 MDS方法.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈21 MDS中加点方法.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈22 矩阵次导数.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈23 矩阵范数.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈24 次导数.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈25 spectral clustering.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈26 K-means alGorithm.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈27 Matr-x Completion.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈28 Fisher判别分析.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈29 谱聚类1 .mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 随机向量性质.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈30 谱聚类2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈31 Computational Methods1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈32 Computational Methods2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈33 Fisher DiscrIMinant Analysis.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈34 Kernel FDA.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈35 Linear classification1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈36 Linear classification2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈37 NAIve Bayes方法.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈38 Support Vector Machines1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈39 Support Vector Machines2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 多元高斯分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈40 SVM.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈41 Boosting1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈42 Boosting2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 分布性质.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 条件期望.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 多项式分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 多元高斯分布及应用.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈9 渐近性质.mp4
│&nBSp;&nBSp;├─机器学习基石_国立台湾大学(林轩田)
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 1 - Course Introduction (10-58)(1).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 2 - What is Machine Learning (18-28).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 3 - APPlications of Machine Learning (18-56)(1).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 4 - Components of Machine Learning (11-45)(1).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈1 - 5 - Machine Learning and Other Fields (10-21)(1).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 1 - Logistic Regression Problem (14-33).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 2 - Logistic Regression Error (15-58).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 3 - GrADient of Logistic Regression Error (15-38).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 - 4 - GrADient Descent (19-18)(1).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 1 - Linear Models for binary Classification (21-35).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 2 - Stochastic GrADient Descent (11-39).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 3 - Multiclass via Logistic Regression (14-18).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 - 4 - Multiclass via binary Classification (11-35).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 1 - QuADratic Hypothesis (23-47).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 2 - Nonlinear Transform (09-52).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 3 - Price of Nonlinear Transform (15-37).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 - 4 - Structured Hypothesis Sets (09-36).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 1 - What is Overfitting- (10-45).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 2 - The Role of Noise and Data Size (13-36).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 3 - DeterMinistic Noise (14-07).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 - 4 - Dealing with Overfitting (10-49).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 1 - Regularized Hypothesis Set (19-16).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 2 - Weight Decay Regularization (24-08).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 3 - Regularization and VC Theory (08-15).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 - 4 - General Regularizers (13-28).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 1 - Model Selection Problem (16-00).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 2 - Validation (13-24).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 - 4 - V-Fold Cross Validation (10-41).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 1 - OccAM-s Razor (10-08).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 2 - SAMpling Bias (11-50).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 3 - Data Snooping (12-28).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 - 4 - Power of Three (08-49).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 1 - Perceptron Hypothesis Set (15-42).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 2 - Perceptron Learning AlGorithm (PLA) (19-46).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 3 - Guarantee of PLA (12-37).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈2 - 4 - Non-Separable Data (12-55).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 1 - Learning with Different Output Space (17-26).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 2 - Learning with Different Data Label (18-12).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 3 - Learning with Different Protocol (11-09).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈3 - 4 - Learning with Different Input Space (14-13).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 1 - Learning is IMpossible- (13-32).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 2 - Probability to the Rescue (11-33).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 3 - Connection to Learning (16-46).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈4 - 4 - Connection to Real Learning (18-06).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 1 - Recap and Preview (13-44).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 2 - Effective Number of Lines (15-26).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 3 - Effective Number of Hypotheses (16-17).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5 - 4 - Break POInt (07-44).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 1 - Restriction of Break POInt (14-18).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 2 - Bounding Function- Basic Cases (06-56).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6 - 4 - A Pictorial Proof (16-01).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 1 - Definition of VC DIMension (13-10).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 2 - VC DIMension of Perceptrons (13-27).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 3 - Physical Intuition of VC DIMension (6-11).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7 - 4 - InterPreting VC DIMension (17-13).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 1 - Noise and Probabilistic Target (17-01).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 2 - Error Measure (15-10).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 3 - AlGorithmic Error Measure (13-46).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈8 - 4 - Weighted Classification (16-54).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 1 - Linear Regression Problem (10-08).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 2 - Linear Regression AlGorithm (20-03).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9 - 3 - Generalization Issue (20-34).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈9 - 4 - Linear Regression for binary Classification (11-23).mp4
│&nBSp;&nBSp;├─机器学习技法_国立台湾大学(林轩田)
│&nBSp;&nBSp;│&nBSp;&nBSp;├─01_Linear_Support_Vector_Machine
│&nBSp;&nBSp;│&nBSp;&nBSp;├─02_Dual_Support_Vector_Machine
│&nBSp;&nBSp;│&nBSp;&nBSp;├─03_Kernel_Support_Vector_Machine
│&nBSp;&nBSp;│&nBSp;&nBSp;├─04_Soft-Margin_Support_Vector_Machine
│&nBSp;&nBSp;│&nBSp;&nBSp;├─05_Kernel_Logistic_Regression
│&nBSp;&nBSp;│&nBSp;&nBSp;├─06_Support_Vector_Regression
│&nBSp;&nBSp;│&nBSp;&nBSp;├─07_Blending_and_Bagging
│&nBSp;&nBSp;│&nBSp;&nBSp;├─08_ADaptive_Boosting
│&nBSp;&nBSp;│&nBSp;&nBSp;├─09_Decision_Tree
│&nBSp;&nBSp;│&nBSp;&nBSp;├─10_Random_Forest
│&nBSp;&nBSp;│&nBSp;&nBSp;├─11_GrADient_Boosted_Decision_Tree
│&nBSp;&nBSp;│&nBSp;&nBSp;├─12_Neural_Network
│&nBSp;&nBSp;│&nBSp;&nBSp;├─13_Deep_Learning
│&nBSp;&nBSp;│&nBSp;&nBSp;├─14_RADial_Basis_Function_Network
│&nBSp;&nBSp;│&nBSp;&nBSp;├─15_Matrix_Factorization
│&nBSp;&nBSp;│&nBSp;&nBSp;└─16_Finale
│&nBSp;&nBSp;├─炼数成金-机器学习
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第1课 机器学习概论
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第2课 线性回归与Logistic。案例：电子商务业绩预测
│&nBSp;&nBSp;│&nBSp;&nBSp;├─第3课 岭回归，Lasso，变量选择技术。案例：凯撒密码破译
│&nBSp;&nBSp;│&nBSp;&nBSp;├─资料
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第10周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第11周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第4周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第5周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第6周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第7周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第8周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈机器学习第9周.rar
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈解压密码.TXT
│&nBSp;&nBSp;├─龙星计划_机器学
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture01（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture02（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture03（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture04（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture05（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture06（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture07（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture08（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture09（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture10（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture11（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture12（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture13（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture14（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture15（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture16（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture17（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture18（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Lecture19_r（更多视频资料关注微信公众号【菜鸟要飞】）.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈下载之前必看！更多视频资料下载目录.docx
│&nBSp;&nBSp;├─模式识别_35_国防科学技术大学(蔡宣平)
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01.概述.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02.特征矢量及特征空间、随机矢量、正态分布特性.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03.聚类分析的概念、相似性测度.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04.相似性测度（二）.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05.类间距离、准则函数.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈06.聚类算法：简单聚类算法、谱系聚类算法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈07.聚类算法：动态聚类算法——C均值聚类算法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈08.聚类算法：动态聚类算法——近邻函数算法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈09.聚类算法实验.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10.判别域界面方程分类的概念、线性判别函数.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11.判别函数值的鉴别意义、权空间及解空间、fisher线性判别.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12.线性可分条件下判别函数权矢量算法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13.一般情况下的判别函数权矢量算法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14.非线性判别函数.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15.最近邻方法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16.感知器算法实验.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17.最小误判概率准则.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18.正态分布的最小误判概率、最小损失准则判决.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈19.含拒绝判决的最小损失准则、最小最大损失准则.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈20.Neyman—Pearson判决、实例.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈21.概述、矩法估计、最大似然估计.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈22.贝叶斯估计.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈23.贝叶斯学习.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈24.概密的窗函数估计方法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈25.有限项正交函数级数逼近法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈26.错误率估计.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈27.小结.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈28.实验3-4-5 Bayes分类器-kNN分类器-视频动目标检测.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈29.概述、类别可分性判据（一）.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈30.类别可分性判据（二）.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈31.基于可分性判据的特征提取.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈32.离散KL变换与特征提取.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈33.离散KL变换在特征提取与选择中的应用.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈34.特征选择中的直接挑选法.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈35.综合实验-图像中的字符识别.flv
│&nBSp;&nBSp;├─统计机器学习_41_上海交大(张志华)
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01 概率基础.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02 随机变量1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03 随机变量2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04 高斯分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05 高斯分布例子.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈06 连续分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈07 jeffrey Prior.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈08 scale mixture pisribarin.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈09 statistic interence.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10 Laplace 变换.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11 多元分布定义.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12 概率变换.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13 Jacobian.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14 Wedge Production.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15 Wishart 分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16 多元正态分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17 统计量.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18 矩阵元Beta分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈19 共轭先验性质.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈20 统计量 充分统计量.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈21 指数值分布.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈22 Entropy.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈23 KL distance.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈24 Properties.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈25 概率不等式1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈26 概率不等式2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈27 概率不等式1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈28 概率不等式2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈29 概率不等式3.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈30 John 引理.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈31 概率不等式.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈32 随机投影.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈33 Stochastic Convergence-概念.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈34 Stochastic Convergence-性质.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈35 Stochastic Convergence-应用.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈36 EM算法1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈37 EM算法2.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈38 EM算法3.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈39 Bayesian Classification.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈40 Markov ChAIn Monte carlo1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈41 Markov ChAIn Monte carlo2.mp4
│&nBSp;&nBSp;└┈南京大学周志华老师的一个讲普适机器学习的PPT【精品-PPT】.PPT
├─ML_机器学习应用班
│&nBSp;&nBSp;├─第八课
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈8.mp4
│&nBSp;&nBSp;├─第二课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈应用班2_1_1h44min.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈应用班第二课第二部分&nBSp;&nBSp;.mp4
│&nBSp;&nBSp;├─第九课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈9-1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈9-2.mp4
│&nBSp;&nBSp;├─第六课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈6-1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈6-2.mp4
│&nBSp;&nBSp;├─第七课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈7-1.flv
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈7-2.mp4
│&nBSp;&nBSp;├─第三课
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈应用班第三节课.mp4
│&nBSp;&nBSp;├─第十课
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈10.mp4
│&nBSp;&nBSp;├─第四课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈第二部分.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈应用班第四节课1_1h44_33.mp4
│&nBSp;&nBSp;├─第五课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈5-1.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈5-2.mp4
│&nBSp;&nBSp;├─第一课
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈第一课.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈机器学习应用班第1课数学基础 (1).pdf
│&nBSp;&nBSp;└┈机器学习应用班资料.zIP
├─算法_10月机器学习算法班
│&nBSp;&nBSp;├─PPT
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈ThumBS.db
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第10讲：推荐系统.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第11讲：CTR预估.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第12讲：聚类和社交网络算法-10月机器学习算法班.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第13讲：机器学习算法之图模型初步.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第15讲：主体模型.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第16讲：人工神经网络.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第17讲：计算机视觉与卷积神经网络.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第18讲：循环神经网络与自然语言处理.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第19讲：深度学习框架与应用.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第1讲.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第20讲：采样与变分.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第2讲.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第3讲：凸优化初步.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第4节：最大熵模型与EM.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第5讲：决策树随机森林.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈十月算法班第8讲：机器学习中的特征工程---笔记版.pdf
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈十月算法班第9讲：机器学习调优与融合.pdf
│&nBSp;&nBSp;├─源码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈IMage_seg.zIP
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈课程PPT与代码.zIP
│&nBSp;&nBSp;├┈01.第1课 概率论与数理统计.mkv
│&nBSp;&nBSp;├┈02.第2课 矩阵和线性代数.mkv
│&nBSp;&nBSp;├┈03.第3课 凸优化.mkv
│&nBSp;&nBSp;├┈04.第4课 回归.mkv
│&nBSp;&nBSp;├┈05.第5课 决策树、随机森林.mkv
│&nBSp;&nBSp;├┈06.第6课 SVM.mkv
│&nBSp;&nBSp;├┈07.第7课 最大熵与EM算法.mkv
│&nBSp;&nBSp;├┈08.第8课 特征工程.mkv
│&nBSp;&nBSp;├┈09.第9课 模型调优.mkv
│&nBSp;&nBSp;├┈10.第10课 推荐系统.mkv
│&nBSp;&nBSp;├┈11.第11课 从分类到CTR预估.mkv
│&nBSp;&nBSp;├┈12.第12课 聚类.mkv
│&nBSp;&nBSp;├┈13.第13课 贝叶斯网络.mkv
│&nBSp;&nBSp;├┈14.第14课 隐马尔科夫模型HMM.mkv
│&nBSp;&nBSp;├┈15.第15课 主题模型.mkv
│&nBSp;&nBSp;├┈16.第16课 采样与变分.mkv
│&nBSp;&nBSp;├┈17.第17课 人工神经网络.mkv
│&nBSp;&nBSp;├┈18.第18课 深度学习之CNN.mkv
│&nBSp;&nBSp;├┈19.第19课 深度学习之RNN.mkv
│&nBSp;&nBSp;└┈20.第20课 深度学习实践.mkv
└─算法_4月机器学习算法班
└─├─(01)机器学习与相关数学初步
└─│&nBSp;&nBSp;├┈(1)机器学习初步与微积分概率论.pdf
└─│&nBSp;&nBSp;└┈(1)机器学习与相关数学初步.avi
└─├─(02)数理统计与参数估计
└─│&nBSp;&nBSp;├┈(2)数理统计与参数估计.avi
└─│&nBSp;&nBSp;└┈(2)数理统计与参数估计.pdf
└─├─(03)矩阵分析与应用
└─│&nBSp;&nBSp;├┈(3)矩阵分析与应用.avi
└─│&nBSp;&nBSp;└┈(3)矩阵分析与应用.pdf
└─├─(04)凸优化初步
└─│&nBSp;&nBSp;├┈(4)凸优化初步.avi
└─│&nBSp;&nBSp;└┈(4)凸优化初步.pdf
└─├─(05)回归分析与工程应用
└─│&nBSp;&nBSp;├─课件和数据及代码
└─│&nBSp;&nBSp;└┈(5)回归分析与工程应用.avi
└─├─(06)特征工程
└─│&nBSp;&nBSp;├─课件与数据及代码
└─│&nBSp;&nBSp;└┈(6)特征工程.avi
└─├─(07)工作流程与模型调优
└─│&nBSp;&nBSp;├┈(7)工作流程与模型调优.avi
└─│&nBSp;&nBSp;└┈(7)工作流程与模型调优.zIP
└─├─(08)最大熵模型与EM算法
└─│&nBSp;&nBSp;├┈(8)最大熵模型与EM算法.avi
└─│&nBSp;&nBSp;└┈(8)最大熵模型与EM算法.pdf
└─├─(09)推荐系统与应用
└─│&nBSp;&nBSp;├─(9)推荐系统与应用
└─│&nBSp;&nBSp;└┈(9)推荐系统与应用.avi
└─├─(10)聚类算法与应用
└─│&nBSp;&nBSp;├┈(10)聚类算法与应用.avi
└─│&nBSp;&nBSp;└┈(10)聚类算法与应用.pdf
└─├─(11)决策树随机森林和ADaboost
└─│&nBSp;&nBSp;├─代码
└─│&nBSp;&nBSp;├┈(11)决策树随机森林ADaboost.avi
└─│&nBSp;&nBSp;└┈(11)决策树随机森林ADaboost.pdf
└─├─(12)SVM
└─│&nBSp;&nBSp;├─(补充材料1)SVM补充视频
└─│&nBSp;&nBSp;├─(补充材料2)SVM的python程序代码
└─│&nBSp;&nBSp;├┈(12)SVM.Avi
└─│&nBSp;&nBSp;├┈(12)SVM.pdf
└─│&nBSp;&nBSp;└┈(12)支持向量机.IPynb
└─├─(13)贝叶斯方法
└─│&nBSp;&nBSp;├┈(13)贝叶斯方法.avi
└─│&nBSp;&nBSp;├┈(13)贝叶斯方法.pdf
└─│&nBSp;&nBSp;└┈nAIve_bayes-master.zIP
└─├─(14)主题模型
└─│&nBSp;&nBSp;├┈(14)主题模型.avi
└─│&nBSp;&nBSp;├┈(14)主题模型.pdf
└─│&nBSp;&nBSp;├┈(补充阅读材料1)Comparing LDA with pLSI as a DIMensionality Reduction Method in Document Clustering.pdf
└─│&nBSp;&nBSp;├┈(补充阅读材料2)Investigating task performance of Probabilistic TOPic models - an empirical stuDY of PLSA and LDA.pdf
└─│&nBSp;&nBSp;└┈LDAClassify.zIP
└─├─(15)贝叶斯推理采样与变分
└─│&nBSp;&nBSp;├┈(15)贝叶斯推理-采样与变分简介.pdf
└─│&nBSp;&nBSp;├┈(15)贝叶斯推理采样变分方法.avi
└─│&nBSp;&nBSp;└┈gibBSGauss.py
└─├─(16)人工神经网络
└─│&nBSp;&nBSp;├┈(16)人工神经网络.avi
└─│&nBSp;&nBSp;├┈(16)人工神经网络.pdf
└─│&nBSp;&nBSp;└┈Lesson_16_Neural_network_exAMple.IPynb
└─├─(17)卷积神经网络
└─│&nBSp;&nBSp;├┈(17)卷积神经网络.avi
└─│&nBSp;&nBSp;└┈(17)卷积神经网络.pdf
└─├─(18)循环神经网络与LSTM
└─│&nBSp;&nBSp;├┈(18)循环神经网络和LSTM.Avi
└─│&nBSp;&nBSp;└┈(18)循环神经网络与LSTM.pdf
└─├─(19)Caffe&AMp;Tensor Flow&AMp;MxNet 简介
└─│&nBSp;&nBSp;├┈(19)Caffe&AMp;Tensor Flow&AMp;MxNet 简介.avi
└─│&nBSp;&nBSp;└┈(19)Caffe&AMp;Tensor Flow&AMp;MxNet 简介.pdf
└─├─(20)贝叶斯网络和HMM
└─│&nBSp;&nBSp;├┈(20)贝叶斯网络和HMM.Avi
└─│&nBSp;&nBSp;└┈(20)贝叶斯网络和HMM.pdf
└─└─(额外补充)词嵌入Word embedding
└─└─├┈(额外补充)词嵌入Word embedding.avi
└─└─└┈(额外补充)词嵌入原理及应用简介.pdf

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