Date	Topics	Readings
Jan 22: Lecture 1	Introduction and Overview Assignment 0 out. Due: January 29.	Chapter 1
Jan 27: Lecture 2	Overview (continued)
Jan 29: Lecture 3	Text mining: representation; dimensionality reduction; similarity and distance measures
Feb 3: Lecture 4	KNN; Evaluation metrics; Types of data; Data preprocessing Assignment 1 Out.	Chapter 2
Feb 5: Lecture 5	Similarity and Distance measures
Feb 10: Lecture 6	Classification (1)
Feb 12: Lecture 7	Classification (2): Decision Trees	Chapter 3
Feb 17: Lecture 8	Classification (3): more on Decision Trees
Feb 19: Lecture 9	Classification (4): Model Evaluation Measures Assignment 1 Due.
Feb 24: Lecture 10	Classification (4): Model Evaluation Measures Assignment 2 Out.
Feb 26: Lecture 11	Classification (5): Instance-based methods; Probability review; Naive Bayes classifier
Mar 2: Lecture 12	Classification (6): Neural networks: perceptron.
Mar 4: Lecture 13	Classification (7): Neural networks: backpropagation.
Mar 9: Spring Break	NO CLASS
Mar 11: Spring Break	NO CLASS
Mar 16: Lecture 14	NO CLASS. Extended Spring Break!
Mar 18: Lecture 15	NO CLASS. Extended Spring Break! Project pitch is postponed!
Mar 23: Lecture 16	SVMs; Bias and Variance Assignment 2 Due
Mar 25: Lecture 17	Ensemble methods
Mar 30: Lecture 18	Project pitch Assignment 3 Out
Apr 1: Lecture 19	Boosting; Clustering (1)
Apr 6: Lecture 20	Clustering (2) Project Proposal Due
Apr 8: Lecture 21	Clustering (3)
Apr 13: Lecture 22	Anomaly Detection
Apr 15: Lecture 23	Anomaly Detection Assignment 3 Due Assignment 4 Out
Apr 20: Lecture 24	Association Rule Mining
Apr 22: Lecture 25	Association Rule Mining Exam
Apr 27: Lecture 26	Association Rule Mining
Apr 29: Lecture 27	Advanced Topics Assignment 4 Due
May 4: Lecture 28	Advanced Topics
May 9:	TBD
May 11:	TBD Project Presentation and Project Report DUE