basic
研一机器学习相关课程作业汇总
1.感知器算法
2.多元线性回归
3.贝叶斯垃圾短信过滤
4.新闻推荐(未完成)
5.Django垃圾短信分类
6.爬虫
Machine Learning
机器学习基石&技法 课程笔记和作业
When can Machines Learn?
1.机器学习问题
2.二分类
3.不同的ML类型
4.可行性
hw0: 条件概率 and 贝叶斯公式
hw1: Perceptron and Pocket algorithm实现
Why can Machines Learn?
1.growthFunction,breakPoint
2.ML泛化理论
3.VC维度,边界
4.噪声和错误估计
hw2:错误率 VC bound计算样本数目N
How can Machines Learn?
1.线性回归,伪逆矩阵,squaredError
2.逻辑回归,sidmod函数,crossEntropyError
3.多分类问题,SGD
4.非线性问题的featureTransform
hw3:损失函数,linear/logistic(SGD) algorithm实现
How can Machines Learn Better?
1.过度拟合的危害,避免的方法
2.有约束的regularizer
3.验证集validation作用
4.小技巧,课程总结
hw4:添加项regularization,验证集valiadation的实现,计算
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
Deep Learning
CS231n课程笔记和作业
https://github.com/cuixuage/2018SpringCS231n
Reinforcement Learning
RLAI 书籍阅读和代码实现
https://github.com/cuixuage/Reinforcement_Learning
Recommend
1.GBDT (xgboost实现)
2.FM (tensorflow实现)
3.FFM
4.diversity(MMR & DPP 实现)
1.机器学习基石&技法 Prof. Hsuan-Tien Lin
2.深度学习CS231n Prof. Fei-Fei Li
3.强化学习RLAI Prof. Richard S.Sutton
4.推荐算法入门级代码 知乎——点击率预估代码
5.计算广告论文 论文汇总
1427 5661 29788 1427 387 2299 372 22 1586 1025 218 165 218 27 7092 22 266 25817 4550 2136 11 60847 9156 1077 797 27 689 3447 11146 108 1667 46829 161702 1113017 548478 24274 5062 46829,2,3447 24 5661 29788 23 75 1678 27 19133 123 29 373 25817 4550 5113