Statistical Learning and Data Mining

SSC 358/CS 363D, Spring 2014
GDC 1.304, Mon & Wed 3:30 - 5:00 pm

Home | Instructor | Syllabus | Homeworks | Projects

Module 1: Data





Types of Data


Preprocessing


Similarity Measures


Module 2: Exploratory Data Analysis




Summary Statistics


Visualization


Multi-dimensional Data Analysis


Module 3: Classification




Decision Trees


Overfitting


Evaluation of Classifiers


Bayes Rule, Naive Bayes


Comparing Classifiers


Misc. Classifiers: SVMs, KNNs


Regression


Module 4: Clustering




K Means


Hierarchical Clustering


Evaluation of a Clustering


Module 5: Association Rules




Frequent Item-sets


Rule Generation


Evaluation


Module 6: Misc. Topics




PCA, SVD


Missing Data


Social Network Analysis


Final Project Poster Presentation