Probability and Statistics
This course presents important probabilistic modeling languages for representing complex domains and how the graphic models extend to decision making. Use ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces. Apply and learn how to construct Probabilistic Graphical Model representation, using both human knowledge and machine learning techniques for good decision making under uncertainty.
- William Feller, Introduction to Probability theory and application
- Trevor Hastie, Robert Tibshirani & Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition Springer Series in Statistics