| Date | Topics | Readings |
|---|---|---|
| Jan 25: Lecture 1 (all sections) | Introduction and Overview Assignment 0 out. |
Chapter 1. [Slides are posted on Blackboard. Please note that this schedule is subject to change.] |
| Jan 27: Lecture 2 | Overview (continued) | |
| Feb 1: Lecture 3 | Assignment 0 Due. 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 (all Sections except: 2.4.7 and 2.4.8) |
| Feb 8: Lecture 5 | Similarity and Distance measures |
|
| Feb 10: Lecture 6 | Classification (1) | |
| Feb 15: Lecture 7 | Classification (2): Decision Trees |
Chapter 3 (Sections: 3.1, 3.2, 3.3, 3.4, 3.5 (except 3.5.2 and 3.5.3), 3.6, 3.8) |
| Feb 17: Lecture 8 |
Classification (3): more on Decision Trees Assignment 1 Due. Assignment 2 Out. |
|
| Feb 22: Lecture 9 | Classification (4): Model Evaluation Measures |
|
| Feb 24: Lecture 10 | Classification (4): Model Evaluation Measures |
|
| Mar 1: Lecture 11 | Classification (5): Instance-based classifiers; Probability review; Naive Bayes classifier |
Chapter 4: Sections 4.1, 4.3, 4.4, 4.7, 4.11. |
| Mar 3: Lecture 12 |
Classification (6): Neural networks: perceptron, multi-layer perceptron, backpropagation. |
|
| Mar 8: Lecture 13 | Midterm Review. | |
| Mar 10: Lecture 14 |
Midterm |
|
| Mar 15: Spring Break | NO CLASS. Spring Break! | |
| Mar 17: Spring Break | NO CLASS. Spring Break! | |
| Mar 22: Lecture 15 |
Support Vector Machines Assignment 2 Due. |
Chapter 4: Section 4.9 |
| Mar 24: Lecture 16 | Bias and Variance Assignment 3 Out. |
Chapter 4: Section 4.10.3 |
| Mar 29: Lecture 17 | Ensemble methods (Bagging and Boosting) |
Chapter 4: Section 4.10 |
| Mar 31: Lecture 18 |
Assignment 3 Due. |
|
| Apr 5: Lecture 19 | Boosting; Clustering (1) | Chapter 7: Sections 7.1, 7.2, 7.3, 7.4, 7.5 |
| Apr 7: Lecture 20 |
Clustering (2) |
|
| Apr 12: Lecture 21 |
Clustering (3) |
|
| Apr 14: Lecture 22 | Anomaly Detection | Chapter 9: 9.1, 9.2, 9.3, 9.4, 9.5 |
| Apr 19: Lecture 23 |
Exercises on K-means, K-medoids, and SVMs |
|
| Apr 21: Lecture 24 | Exercises on clustering validation and hierarchical clustering | |
| Apr 26: Lecture 25 |
Recommendation Systems Assignment 4 Out |
|
| Apr 28: Lecture 26 |
Association Rule Mining |
Chapter 5: Sections 5.1, 5.2, 5.3 (5.3.1 and 5.3.2), 5.7: limitations of support and confidence; multiple support measures; lift or interest factor. (See slides for guidance.) |
| May 3: Lecture 27 | Association Rule Mining |
|
| May 5 Lecture 28: | Association Rule Mining Assignment 4 Due |
|
| May 12: | Final Exam |