• Linear Models
  • Box-Cox Transformations, generating Y (pg 324)
  • Test for Heteroskedasticity (pg 329)
  • Variable Selection (pg 359)
  • Ridge Regression/LASSO
  • Generalized Linear Models + Logistic Regression
  • Model Selection Criteria (Ch. 22)
• Stat 154
  • PLSR
  • KNN/Kernels
  • Discriminant Analysis
  • Hierarchal Clustering + Clustering
  • Random Forests
• Deep Learning w. Tensorflow or PyTorch
• Data pipeline (k-fold, bagging, etc.)
• implement NestedCV (for all of model selection)

Prioritize LASSO (for variable selection), and model Selection Then everything in Stat154 you think is interesting

• Learn about
  • SVM/SVC
  • Boosting
  • Perceptron

Idea: 1. Cross-validate for hyper-parameters, parameters, etc. 2. Then try bagging/boosting

LDA ESL pg 439

Create correlation matrix for inputs Bayesian hyperparameter tuning