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 <intro-beamer.pdf> handouts <intro-handout.pdf> scans <intro-scans.pdf> |
| Supervised Learning::Linear Models | |
| Linear Regression | slides <linear-regression-beamer.pdf> handouts <linear-regression-handout.pdf> scans <linear-regression-scans.pdf> |
| Logistic Regression/Percepton | slides <logistic-regression-beamer.pdf> handouts <logistic-regression-handout.pdf> scans <logistic-regression-scans.pdf> |
| Support Vector Machines | slides <linear-svm-beamer.pdf> handouts <linear-svm-handout.pdf> scans <linear-svm-scans.pdf> |
| Kernel Methods | |
| Kernel Regression | slides <kernel-regression-beamer.pdf> handouts <kernel-regression-handout.pdf> scans <kernel-regression-scans.pdf> |
| Kernel Support Vector Machines | slides <kernel-svm-beamer.pdf> handouts <kernel-svm-handout.pdf> scans <kernel-svm-scans.pdf> |
| Supervised Learning::Non-linear Models | |
| Non-linear Regression and Regularization | slides <nonlinear-regression-beamer.pdf> handouts <nonlinear-regression-handout.pdf> scans <nonlinear-regression-scans.pdf> |
| Neural Networks | slides <neural-networks-beamer.pdf> handouts <neural-networks-handout.pdf> scans <neural-networks-scans.pdf> |
| Statistical Learning | |
| Generative Models | slides <generative-models-beamer.pdf> handouts <generative-models-handout.pdf> scans <generative-models-scans.pdf> |
| Bayesian Learning | slides <bayesian-learning-beamer.pdf> handouts <bayesian-learning-handout.pdf> scans <bayesian-learning-scans.pdf> |
| Bayesian Classification | slides <bayesian-classification-beamer.pdf> handouts <bayesian-classification-handout.pdf> scans <bayesian-classification-scans.pdf> |
| Bayesian Linear Regression | slides <bayesian-regression-beamer.pdf> handouts <bayesian-regression-handout.pdf> scans <bayesian-regression-scans.pdf> |
| Fairness in Machine Learning | |
| Fairness aspects in Machine Learning | slides <fairness-ml-beamer.pdf> handouts <fairness-ml-handout.pdf> scans <fairness-ml-scans.pdf> |
| Fairness primer | fairness primer <Machine_Learning_Fairness_Primer.pdf> |
| Decision Trees | slides <decision-trees-beamer.pdf> handouts <decision-trees-handout.pdf> scans <decision-trees-scans.pdf> |
| Unsupervised Learning | |
| Clustering (k-Means/Spectral Methods) | slides <clustering-algorithms-beamer.pdf> handouts <clustering-algorithms-handout.pdf> scans <clustering-algorithms-scans.pdf> |
| Principal Component Analysis | slides <principal-component-analysis-beamer.pdf> handouts <principal-component-analysis-handout.pdf> scans <principal-component-analysis-scans.pdf> |