Approximate CS578 Syllabus

Class Date Day Topic
1 8/25 Thu Statistics, Machine Learning, and Data Mining
2 8/30 Tue Decision Trees: Recursive Partitioning, Splitting Rule
3 9/01 Thu Decision Trees: Efficiency, Pruning, Converting Trees to Rules
4 9/06 Tue Train/Test Set Splits
5 9/08 Thu Artificial Neural Nets: Backpropagation
6 9/13 Tue Artificial Neural Nets: Overfitting (Weight Decay, Early Stopping, and Network Size)
7 9/15 Thu Artificial Neural Nets: Multitask Learning
8 9/20 Tue Cross Validation
9 9/22 Thu Feature Selection
10 9/27 Tue Data Preprocessing: Attribute Transformation & Missing Values
11 9/29 Thu K-Nearest Neighbor
12 10/04 Tue Boosting & Bagging and the Bias/Variance Tradeoff
13 10/06 Thu Boosting & Bagging
--- 10/11 Tue No Class -- Fall Break
14 10/13 Thu SVMs
15 10/18 Tue SVMs
16 10/20 Thu Performance Metrics: Accuracy, ROC, RMSE, ...
17 10/25 Tue Empirical Comparison of Learning Methods
18 10/27 Thu
19 11/01 Tue Data Mining of Association Rules
20 11/03 Thu Agglomerative Clustering
21 11/08 Tue K-Means Clustering & EM (Expectation Maximization)
22 11/10 Thu Scaling Clustering to Large Data Sets
23 11/15 Tue Fractal Dimension
24 11/17 Thu Multi-dimensional Scaling
25 11/22 Tue
--- 11/24 Thu No Class -- Thanksgiving
26 11/29 Tue Case Study: Pneumonia Risk Prediction (Prospective Analysis)
27 12/01 Thu Case Study: Protein Folding (Clustering, Visualization, & Discovery)
--- 12/09 Thu Final Exam (open book)    9:00am-11:30am  Place: TBA