DocumentsΒΆ
Note
The handouts have all the content that the slides have, along with some additional discussion which is not on the slides. If you want to save these for future use or for printing, please use the handouts and not the slides.
| Topic | Documents |
|---|---|
| ML Basics | slides handouts scans |
| Supervised Learning::Linear Models | |
| Linear Regression | slides handouts scans |
| Logistic Regression/Percepton | slides handouts scans |
| Support Vector Machines | slides handouts scans |
| Kernel Methods | |
| Kernel Regression | slides handouts scans |
| Kernel Support Vector Machines | slides handouts scans |
| Supervised Learning::Non-linear Models | |
| Non-linear Regression and Regularization | slides handouts scans |
| Neural Networks | slides handouts scans |
| Statistical Learning | |
| Generative Models | slides handouts scans |
| Bayesian Learning | slides handouts scans |
| Bayesian Classification | slides handouts scans |
| Bayesian Linear Regression | slides handouts scans |
| Fairness in Machine Learning | |
| Fairness aspects in Machine Learning | slides handouts scans |
| Fairness primer | fairness primer |
| Decision Trees | slides handouts scans |
| Unsupervised Learning | |
| Clustering (k-Means/Spectral Methods) | slides handouts scans |
| Principal Component Analysis | slides handouts scans |