https://github.com/cuixuage/Machine_Learning
Lecture Directory
How can machines learn by Embedding numerous features
1.线性SVM,推导非条件目标,QP求解
2.对偶SVM,非线性问题消除Z域d+1依赖
3.kernel trick仅在X域计算
4.soft-margin,ξn
5.KLR,two-level-learning模拟Z域逻辑回归
6.SVR,tube regression
hw1:soft-margin SVM分类,linear,poly,rbf实验
How can machines learn by Combining predictive features
1.blending,bagging,bootstrap获取多样性gt
2.adaboost,惩罚因子Ut
3.decisionTree,impurity衡量
4.randomForest,feature-selection
5.GBDT,residual fitting
hw2:Adaboost-stump 未完成
hw3:cart tree,random forest 未完成
how can machines learn by distilling hidden features?
1.NeuralNetwork,backprop,optimization Tricks
2.DeepLearning,pre-trained autoencoder,denoising
3.RBFnetwork,distance similarity,k-means algorithm
4.linear network,alternating leastSQR
5.feature exaction,optimization,overfitting
hw4:NNet 未完成,k-nearest-means,k-means