Bulletin Description
Prerequisites: Graduate standing; MATH 448; or permission of the instructor.
Study of the fundamental concepts of statistical and machine learning theory.
Student Learning Outcomes
Upon completion of this course a student should be able to
- Understand the statistical principals and reasoning behind modern supervised & unsupervised learning methods.
- Learn the whole process of creating data models, including data pre- processing, feature selection, model fitting, and model selection & evaluation.
- Master the skills to perform data analysis using computer software, to interpret results and to communicate results effectively.
- Obtain the hands-on experience by analyzing real data sets and make decent presentation about the results to audiences.
Course Outline
- Statistical Decision Theory (1 week)
- Support Vector Machines (2 weeks)
- Discriminant Analysis (2 week)
- Shrinkage Methods (2 weeks)
- Ensemble Learning (2 weeks)
- High-dimensional Classification (1 week)
- Unsupervised Learning (3 weeks)
- Feature Selection (1 week)
Textbooks & Software
- An Introduction to Statistical Learning, with applications in R (2013), G. James, D. Witten, T. Hastie, R. Tibshirani.
- The Elements of Statistical Learning, Data Mining, Inference and Prediction (2nd edition), Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Applied Predictive Modeling (2013), Max Kuhn, Kjell Johnson
- R, by R Development Core Team