Machine Leaning Class Lectures

Outline

1. Introduction to Machine Learning
2. Supervised Learning
3. Classification, KNN
4. Decision Trees
5. Classification Performance Metrics
6. Bayes classifiers
7. Support Vector Machine
8. –
9. Regression
10. Dimensionality Reduction/ Feature Selection
11. Cluster Analysis (K-Means)
12. Hierarchical Clustering
13. Association Rule Mining
14. Ensemble Learning
15. Wrap-Up (Recap)

Notes

• Code and supplementary data from lectures will be shared here
• You can use any IDE of your choice, however jupyter is recommended for this course