Texts: Larsen and Marx, An Introduction to
Mathematical Statistics and its Applications, 5th ed.
Pattern Recognition and Machine Learning, C. Bishop
Course outline: The first part of the course will cover Estimation, Hypothesis Testing and Regression from a rigorous point of view. The second will focus on data analysis, pattern recognition, classification, discriminant analysis and other topics as time permits. The course will be taught by Richard Kenyon and Isaac Solomon.
There will be homeworks collected weekly and one midterm. The final grade will be weighted as follows: Homework 35%, midterm 20%, final 45%.