Date	Topics	Readings
Jan 22: Lecture 1	Introduction and Overview Assignment 0 out. Due: January 30.	Chapter 1
Jan 24: Lecture 2	Overview (continued)
Jan 29: Lecture 3	Text mining: representation; dimensionality reduction; similarity and distance measures
Jan 31: Lecture 4	KNN; Evaluation metrics; Types of data; Data preprocessing Assignment 1 Out.	Chapter 2
Feb 5: Lecture 5	Similarity and Distance measures
Feb 7: Lecture 6	Classification (1)
Feb 12: Lecture 7	Classification (2): Decision Trees	Chapter 3
Feb 14: Lecture 8	Classification (3): more on Decision Trees
Feb 19: Lecture 9	Classification (4): Model Evaluation Measures Assignment 1 Due.
Feb 21: Lecture 10	Classification (4): Model Evaluation Measures
Feb 26: Lecture 11	Classification (5): Instance-based methods; Probability review; Naive Bayes classifier
Feb 28: Lecture 12	Classification (6): Neural networks: perceptron. Assignment 2 Out.
Mar 5: Lecture 13	Classification (7): Neural networks: backpropagation.
Mar 7: Lecture 14	Project pitch
Mar 12: Spring Break	NO CLASS
Mar 14: Spring Break	NO CLASS
Mar 19: Lecture 15	SVMs Assignment 2 Due.
Mar 21: Lecture 16	Bias and Variance Project Proposal Due.
Mar 26: Lecture 17	Ensemble methods; Clustering Assignment 3 Out.
Mar 28: Lecture 18	Clustering (1)
Apr 2: Lecture 19	Clustering (2)
Apr 4: Lecture 20	Clustering (3)
Apr 9: Lecture 21	Exam: in class and closed book.
Apr 11: Lecture 22	Anomaly Detection Assignment 3 Due. Assignment 4 Out.
Apr 16: Lecture 23	Association Rule Mining
Apr 18: Lecture 24	Association Rule Mining
Apr 23: Lecture 25	Association Rule Mining
Apr 25: Lecture 26	Assignment 4 Due.
Apr 30: Lecture 27	TBD
May 2: Lecture 28	Project Presentation Due.
May 9:	Project Report Due.