ISLR :: 소개 및 강좌
Course: https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about
https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
Book: www.StatLearning.com http://www-bcf.usc.edu/~gareth/ISL/
http://www.springer.com/series/417
번역본: 가볍게 시작하는 통계학습 (컬러가 아닌게 함정)
참고: http://www.alsharif.info/iom530
An Introduction to Statistical Learning, with Applications in R”
(James, Witten, Hastie, Tibshirani – Springer 2013
W1: Introduction and Overview of Statistical Learning (Chapters 1-2)
W2: Linear Regression (Chapter 3)
W3: Classification (Chapter 4)
W4: Resampling Methods (Chapter 5)
W5: Linear Model Selection and Regularization (Chapter 6)
W6: Moving Beyong Linearity (Chapter 7)
W7: Tree-based Methods (Chapter 8)
W8: Support Vector Machines (Chapter 9)
W9: Unsupervised Learning (Chapter 10)
Chapter 1: Introduction (slides, playlist)
- Opening Remarks and Examples (18:18)
- Supervised and Unsupervised Learning (12:12)
Chapter 2: Statistical Learning (slides, playlist)
- Statistical Learning and Regression (11:41)
- Curse of Dimensionality and Parametric Models (11:40)
- Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
- Classification Problems and K-Nearest Neighbors (15:37)
- Lab: Introduction to R (14:12)
Chapter 3: Linear Regression (slides, playlist)
- Simple Linear Regression and Confidence Intervals (13:01)
- Hypothesis Testing (8:24)
- Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
- Model Selection and Qualitative Predictors (14:51)
- Interactions and Nonlinearity (14:16)
- Lab: Linear Regression (22:10)
Chapter 4: Classification (slides, playlist)
- Introduction to Classification (10:25)
- Logistic Regression and Maximum Likelihood (9:07)
- Multivariate Logistic Regression and Confounding (9:53)
- Case-Control Sampling and Multiclass Logistic Regression (7:28)
- Linear Discriminant Analysis and Bayes Theorem (7:12)
- Univariate Linear Discriminant Analysis (7:37)
- Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
- Quadratic Discriminant Analysis and Naive Bayes (10:07)
- Lab: Logistic Regression (10:14)
- Lab: Linear Discriminant Analysis (8:22)
- Lab: K-Nearest Neighbors (5:01)
Chapter 5: Resampling Methods (slides, playlist)
- Estimating Prediction Error and Validation Set Approach (14:01)
- K-fold Cross-Validation (13:33)
- Cross-Validation: The Right and Wrong Ways (10:07)
- The Bootstrap (11:29)
- More on the Bootstrap (14:35)
- Lab: Cross-Validation (11:21)
- Lab: The Bootstrap (7:40)
Chapter 6: Linear Model Selection and Regularization (slides, playlist)
- Linear Model Selection and Best Subset Selection (13:44)
- Forward Stepwise Selection (12:26)
- Backward Stepwise Selection (5:26)
- Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared (14:06)
- Estimating Test Error Using Cross-Validation (8:43)
- Shrinkage Methods and Ridge Regression (12:37)
- The Lasso (15:21)
- Tuning Parameter Selection for Ridge Regression and Lasso (5:27)
- Dimension Reduction (4:45)
- Principal Components Regression and Partial Least Squares (15:48)
- Lab: Best Subset Selection (10:36)
- Lab: Forward Stepwise Selection and Model Selection Using Validation Set (10:32)
- Lab: Model Selection Using Cross-Validation (5:32)
- Lab: Ridge Regression and Lasso (16:34)
Chapter 7: Moving Beyond Linearity (slides, playlist)
- Polynomial Regression and Step Functions (14:59)
- Piecewise Polynomials and Splines (13:13)
- Smoothing Splines (10:10)
- Local Regression and Generalized Additive Models (10:45)
- Lab: Polynomials (21:11)
- Lab: Splines and Generalized Additive Models (12:15)
Chapter 8: Tree-Based Methods (slides, playlist)
- Decision Trees (14:37)
- Pruning a Decision Tree (11:45)
- Classification Trees and Comparison with Linear Models (11:00)
- Bootstrap Aggregation (Bagging) and Random Forests (13:45)
- Boosting and Variable Importance (12:03)
- Lab: Decision Trees (10:13)
- Lab: Random Forests and Boosting (15:35)
Chapter 9: Support Vector Machines (slides, playlist)
- Maximal Margin Classifier (11:35)
- Support Vector Classifier (8:04)
- Kernels and Support Vector Machines (15:04)
- Example and Comparison with Logistic Regression (14:47)
- Lab: Support Vector Machine for Classification (10:13)
- Lab: Nonlinear Support Vector Machine (7:54)
Chapter 10: Unsupervised Learning (slides, playlist)
- Unsupervised Learning and Principal Components Analysis (12:37)
- Exploring Principal Components Analysis and Proportion of Variance Explained (17:39)
- K-means Clustering (17:17)
- Hierarchical Clustering (14:45)
- Breast Cancer Example of Hierarchical Clustering (9:24)
- Lab: Principal Components Analysis (6:28)
- Lab: K-means Clustering (6:31)
- Lab: Hierarchical Clustering (6:33)
Interviews (playlist)
- Interview with John Chambers (10:20)
- Interview with Bradley Efron (12:08)
- Interview with Jerome Friedman (10:29)
- Interviews with statistics graduate students (7:44)
참고> http://psystat.tistory.com/category/R/ISLR
