Topics:
- Statistical learning: (Supervised learning - Unsupervised learning)
- Linear Regression
- Classification: ( Logistic Regression, Bayes Classfier)
- Linear Model Selection and Regularization: (Ridge Regression, Lasso Regression)
- Decision Trees: (Bagging, Random Forests, Boosting)
- Clustering: (K-means, Hierarchical, Model-based: Mixture models)
- Neural Networks (A brief introduction)
TextBook:
Moset of the topics are based on:
- An Introduction to Statistical Learning: with Applications in R (2013) (Springer Series in Statistics) by G. James, D. Witten, T. Hastie and R. Tibshirani
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) (2001 & 2009) by T. Hastie, R. Tibshirani, J. H. Friedman.