STAT571 DATA MINING AND MACHINE LEARNING
| Course Code: |
2460571 |
| METU Credit (Theoretical-Laboratory hours/week): |
3(3-0) |
| ECTS Credit: |
8.0 |
| Department: |
Statistics |
| Language of Instruction: |
English |
| Level of Study: |
Graduate |
| Course Coordinator: |
|
| Offered Semester: |
Fall and Spring Semesters. |
Course Content
Unsupervised learning. Principal component analysis (PCA), clustering methods. Rule learning, association rules. Supervised learning. Multiple linear regression. K-nearest neighbors. Logistic regression. Linear discriminant analysis. Linear model selection. Regularization techniques. Ridge regression, LASSO. Splines. Generalized additive models (GAMs). Tree-based methods. Ensemble learning. Bagging, random forest, boosting. Support vector machines. Neural networks and deep learning. Evaluating the performance of machine learning algorithms. No Free Lunch theorems. Bias-variance decomposition. Bagging. Boosting. Generative adversarial networks (GANs). Autoencoders. Variational Autoencoders.
