A brief talk about machine learning in Python

0. Linear regression

   • which one would be the best classifer while having many possibility to perfectly discriminate data?
   • maximal margin => SVM

1. SVM

   • Mathematical formulation
   • relation to logistic regression in view of loss function
   • kernel trick (linear, poly, rbf)

2. RandomForest

   • 原理
   • Decision Tree
   • Pros and Cons

3. XGBoost

   • 原理
   • the difference with GBDT
   • Gradient Boosting Method
   • Ensemble of a series of randomforests with gradient boosting

1. Dimension reduction

   • why we need dimension reduction?
   • how to find out the principal components from observed data?

2. 說明 PCA 原理

   • (1) linear combination of original features, (2) explain the most variance in that
     • http://web.ntpu.edu.tw/~ccw/statmath/M_pca.pdf
   • 什麼是 explained variance: 投影到某一軸, data variance 能夠被表達多少
     • https://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained
     • https://stats.stackexchange.com/questions/254592/calculating-pca-variance-explained/254598
     • an example to verify
   • PCA example and visualization

3. Other feature selection method

   • Lasso regression

4. Clustering

   • Kmeans clustering
   • Minibatch Kmeans clustering
   • GMM (Gaussian Mixed Model)
   • Kernel Approximation