| Week | Content | HW | Due | Reading | |
| week 1 | |||||
| Friday 9/1 |
Intro | Chapter 1, 2, 3 | |||
| week 2 | Tuesday 9/5 |
Preprocessing | |||
| Friday 9/8 |
KNN | HW1 | Chapter 9.5 | ||
| week 3 | Tuesday 9/12 |
Decision Tree | Chapter 8.1, 8.2 | ||
| Friday 9/15 |
Random Forest | ||||
| week 4 | Tuesday 9/19 |
Linear Regression | Learning
From Data: 2.32, 3.2, 3.32, 4.22, 4.3 |
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| Friday 9/22 |
Regularization | HW2 | HW1 | ||
| week 5 | Tuesday 9/26 |
Bias_Variance
Trade-off Cross-Validation (CV) |
Bias-Variance Trade-off Chapter 9.5 |
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| Friday 9/29 |
Perceptron/SVM | Chapter
9.3 Bishop Chapter 7.1 Perceptron Convergence Introduction to Weka |
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| week 6 | Tuesday 10/3 |
SVM | |||
| Friday 10/6 |
Kernel Project Intro |
HW3 group formation |
HW2 |
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| week 7 | Tuesday 10/10 |
Probability
Review Naïve Bayes |
Chapter 8.3 | ||
| Friday 10/13 |
Naïve Bayes | ||||
| week 8 | Tuesday 10/17 |
Review | |||
| Friday 10/20 |
Midterm | HW3 group formation |
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| week 9 | Tuesday 10/24 |
Review
midterm Project Intro |
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| Friday 10/27 |
Feature Selection | HW4 | |||
| week 10 | Tuesday 10/31 |
Missing
Data Imbalance Data |
Imbalanced Data | ||
| Friday 11/3 |
Ensemble | ||||
| week 11 | Tuesday 11/7 |
Hierarchical Clustering | Chapter
10 Bishop 9.1 Clustering Methods |
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| Friday 11/10 |
K-means | HW5 | HW4 | ||
| week 12 | Tuesday 11/14 |
K-mediods Clustering Evaluation |
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| Friday 11/17 |
Apriori | Chapter
6 Tan, Steinbach and Kumar |
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| week 13 | Tuesday 11/21 |
Apriori | |||
| Friday 11/24 No Class |
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| week 14 | Tuesday 11/28 |
GSP | HW6 | HW5 | GSP |
| Friday 12/1 |
Autoregression | ||||
| week 15 | Tuesday 12/5 |
DTW | |||
| Friday 12/8 |
Review | HW6 project |
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| week 16 | Tuesday 12/12 No Class |
Reading Period | |||
| Friday 12/15 |
Final |