@(131 - Machine Learning | 机器学习) 零. Goal How Unsupervised Learning fills in that model gap from the original Machine Learning work flow 2.How to compare different models developed using Unsupervised Learning for their relative strengths and relative…
@(131 - Machine Learning | 机器学习) 1 Feature Scaling transforms features to have range [0,1] according to the formula $x' = \frac{x-x_{min}}{x_{max}-x_{min}} $ 1.1 Sklearn - MinMaxScaler from sklearn.preprocessing import MinMaxScaler import numpy weigh…
@(131 - Machine Learning | 机器学习) PCA是一种特征选择方法,可将一组相关变量转变成一组基础正交变量 25 PCA的回顾和定义 Demo: when to use PCA latent features driving the patterns in the data (demo find the big shots in enron) 访问隐藏的特征 dimensionality reduction 1)visualize high dimensional dat…
1 Why? Reason1 Knowledge Discovery (about human beings limitaitons) Reason2 Cause of Dimensionality (维度灾难) (about ML algorithm itself) 所需的数据量会根据你所拥有的特征数量以指数速度增长 2 NP-Hard Problem arbitrarily choose m features from n features (m≤n),don't know what m t…
Supervised Learning Unsupervised Learning Reinforced Learning Goal: How to apply these methods How to evaluate each methods What is Machine Learning? 1.computational statistics 2.computational artifacts(人工制品) that learn over time based on experience…
