A collection of codes implementing various ML algorithms in R, mostly done for course STAT542: Statistical Learning
- KNN - KNN.pdf
- Gradient descent Optimisation- gradient.pdf
- Linear Model Selection using AIC/BIC/Cp and Cross Validation - linear_model_selection.pdf
- Coding your own LASSO - LASSO.pdf
- Splines comparison (Linear, Quadratic, Natural cubic, Smoothing splines) - splines.pdf
- Multi-dimensional Kernel and Bandwidth Selection - kernels.pdf
- Tuning Random Forests in Virtual Twins (Personalized medicine) - randomforest.pdf
- Code your own K-means (Clustering handwritten digits) - kmeans.pdf
- Expectation Maximization algorithm (Two dimensional gaussian mixture model) - gaussion_mixture.pdf
- SVM (Linear, non-linear, seperable and non-seperable) - svm.pdf
- Boosting (Code your own Adaboost) - adaboost.pdf