Machine Learning I.
Lectures | Topics |
L1 | Supervised Learning, OLS, Ridge, Lasso, Regularization, Cross- Validation, Nonparametric Regression (kernel, spline), Additive Models |
L2 | Classification, Logistic Regression, ROC, Calibration Plot |
L3 | CART Algo, Bootstrap, Bagging, Random Forest, Boosting, XGBoost |
Machine Learning II.
Lectures | Topics |
L1 | LDA, QDA, Naive Bayes |
L2 | SVM, Anomaly Detection |
L3 | Matrix Factorization, Mixture Models (NMF, EM) |
L4 | Model Evaluation and Deployment |
L5 | Survival Analysis and Pointe Processes |