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STATISTICAL LEARNING AND DATA MINING

Outline:

  1. Introduction
  2. Statistical Learning
  3. Linear Regression
  4. Classification
  5. Resampling Methods
  6. Linear Model Selection and Regularization
  7. Moving Beyond Linearity
  8. Tree-Based Methods
  9. Support Vector Machines
  10. Unsupervised Learning

Objective:

Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine l earning. This course encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines.

Textbook:

  1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R
  2. Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Second Edition.
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