Lecture Slides

1. Introduction to the Course, nb-nm, nb-lr

2. Multiple Linear Regression

3. Linear & Logistic Regression

4. Implementing Logistic Regression, nb-lrmse

5. Bayes Classifier (1)

6. 6. Bayes Classifier

7. MLE and Naive Bayes (1)

8. Naive Bayes, Logistic Regression

9. Softmax Regression, kNN Classification

10. Principal Component Analysis, nb-pca

11. Data Clustering, nb-km

------- Mid-Semester -------

12. Feature Scaling, Distance Metrics

13. Lasso & Ridge Regression

14. Multi-Layered Perceptrons

15. Neural Networks and Deep Learning, nb-nn

16. Classification Validation

17. ROC Analysis, No Free Lunch, PAC Learning

18. VC Dimensions, Support Vector Machines (1)

19. Support Vector Machines (2)

20. RNNs, Imbalanced Classification

21. Decision Trees

22. AdaBoost, Random Forests

23. k-Means variants and Hierarchical Clustering

24. (Guest Lecture) Multi-Arm Bandits, by Saptam Bakshi

25. (Guest Lecture) Introduction to Reinforcement Learning, by Srinjoy Roy

------- Final Semester -------