Essentials of Statistical Learning (the Elements of statistical Learning) class notes series
- Posted at January 2nd, 2014
- Filed under
Course Material: The Elements of statistical learning http://www-stat.stanford.edu/~tibs/ElemStatLearn/
Lecturer: Professor Wulide, School of Computer Science, Fudan University
Lesson NOTES:
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (i): Introduction and Curriculum outline
- Essentials of Statistical Learning (the Elements of statistical Learning) class notes (ii): Simple prediction method, OLS and KNN, statistical decision theory
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (iii): high-dimensional spatial problems, linear regression methods
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (iv): OLS and Gauss-Markov theorem
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (V): Logit and LDA
- Essentials of Statistical Learning (the Elements of statistical Learning) class notes (vi): Logisitic, LDA, and Perceptional classifiers
- Essentials of Statistical Learning (the Elements of statistical Learning) class notes (vii): B-splines (spline)
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (eight): Smoothing splines, wavelet analysis
- Essentials of Statistical Learning (the Elements of statistical Learning) class notes (ix): Nuclear smoothing and Local methods
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (10): MM, EM and GMM
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (11): BootStrap, MLE
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (12): Additive models, tree models
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (13): MARS, PRIM, HME, base function model
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (14): Boost (AdaBoost), adaptive basis function model, forward distribution algorithm, exponential loss function
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (15): Gradient Tree Lifting algorithm (GTBA)
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (16): Random forest (randomly Forest)
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (17): Neural network
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (18): Neural network
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (19): SVM
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (20): SVM
- Essentials of Statistical Learning (the Elements of statistical Learning) class notes (21): SMO algorithm
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (22): Nuclear functions and nuclear methods
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (23): Prototype method and nearest neighbor KNN
- Essentials of Statistical Learning (the Elements of statistical Learning) class notes (24): Clustering
- Essentials of Statistical Learning (the Elements of statistical Learning) lecture notes (25): Descending and PCA
Essentials of statistical learning
