This repo is my studying notes of An Introduction to Statistical Learning: With Applications in R, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, and other Machine Learning books. Check a more user friendly version on my personal website here.
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Linear Regression
- Linear Regression Models
Simple Linear Regression Models; Multiple Linear Regression; Comparison of Linear Regression with K-Nearest Neighbors
- Potential Problems
- Example: Prostate Cancer
- Linear Regression Models
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Classification
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The Logistic Model; Estimating the Regression Coefficients; Multiple Logistic Regression
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Using Bayes’ Theorem for Classification; Linear Discriminant Analysis for p >1; Quadratic Discriminant Analysis
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Resampling Methods
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Model Selection and Assessment
- Assessing Model Accuracy
Measuring the Quality of Fit; The Bias-Variance Trade-Off; Classification Setting
- Model Selection and Regularization
Subset Selection; Shrinage Methods; Considerations In High Dimensions
- Bias, Variance and Model Complexity
- Assessing Model Accuracy
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Tree-Based Methods
- Decision Trees, Random Forest, and Boosting
Descision Tree; Bagging; Random Forest; Boosting
- Decision Trees, Random Forest, and Boosting
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Maximal Margin Classifier; Support Vector Classifiers; Support Vector Machines
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Unsupervised Learning
- Clustering
Clustering Methods; Hierarchical Clustering; Practical Issues in Clustering
- Dimension Reduction - PCA, PCR
Dimension Reduction Methods; Principal Components Regression
- Clustering
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James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.
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Hastie, Trevor, et al. "The elements of statistical learning: data mining, inference and prediction." The Mathematical Intelligencer 27.2 (2005): 83-85
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Rice, John A. Mathematical statistics and data analysis. Cengage Learning, 2006.