### Discovery, Winter, 2010-2011

Course Number: 4040-820-01
Time: TuTh / 12-1:50 PM
Room:70-1610
Website: people.rit.edu/~jcdicsa/courses/Discovery/
Instructors: Justin Domke and Linwei Wang

Course Text: All of Statistics, by Wasserman and Advanced Linear Algebra for Engineers with MATLAB, by Dianat and Saber.

Grading Scheme:

• 15% Homework
• 10% Participation
• 45% Presentations
• 30% Final

Lectures:
 Date Topic Reading Homework Tuesday, Nov. 30 Probability Chapter 1 Prove Bayes' Theorem (strong version) + Chapter 1, problems 5, 10, 11, 12, 13, 15, 19, 20 (Due Friday, Dec. 2) Thursday, Dec. 2 Random Variables 2.1, 2.2, 2.3 on Bernoulli, 2.4 on Normal, 2.5, 2.6 Chapter 2, problems 4, 5, 10, 11(a), 14, 20 (Due Tuesday, Dec. 14) Monday, Dec. 6 8:00am - 9:50am in 78-2015 note special time/location! Finish Random Variables, Expectations, Variance, Covariance, Markov's Inequality 2.7, 2.8, 2.9, 3.1, 3.2, 3.3, 3.5, 4.1 Tuesday, Dec. 7 (no class!) Thursday, Dec. 9 Matlab Tutorial Tuesday, Dec. 14 Hoeffding's Inequality 4.1 Prove V(X)=E[X^2]-(E[X])^2. + Chapter 3, problems 1, 3, 5, 6, 13, 21 (Due Friday Dec. 17 6pm) Thursday, Dec. 16 Point Estimation, Confidence Intervals Chapter 6 Chapter 6, problems 1, 2, 3 (Due Jan 4, 2011) (break) Tuesday, Jan. 4 (Last Statistics Lecture) Chapter 10, problems 7(b), and 8. (Due Jan. 11) Thursday, Jan. 6 Overview of Machine Learning Tuesday, Jan. 11 Student Presentations on Statistics 1: Emma, Sam, Nishant (see below) Thursday, Jan. 13 Student Presentations on Statistics 2: Abdul, Azar, Ricardo, Lei (see below) Tuesday, Jan. 18 (Begin Linear Algebra Section)

Final Exam: Tuesday Feb. 22, 2011, 12:30pm - 2:30pm (Location TBD)

Student Presentation Options for Statistics

• Convergence of Random Variables (5.1, 5.2, 5.3, 5.4) (Emma)
• Cross Validation (Azar)
• The Bootstrap (Chapter 8)
• Statistical Decision Theory (12.1, 12.2, 12.3, 12.4, 12.6)
• Linear and Logistic Regression. (13.1, 13.2, 13.3, 13.4, 13.7) (Ricardo)
• Directed Graphs (Chapter 17) (Abdul)
• Undirected Graphs (Chapter 18) (Nishant)
• Log-Linear Models (Chapter 19)
• Smoothing Using Orthogonal Functions (21.1, 21.2, 21.3, 21.4)
• Classification (Chapter 22) (Lei)
• Statistical Modeling: The Two Cultures, Leo Breiman, Statistical Science 2001, Vol. 16, No. 3. You should also cover the commentary that comes after the paper. (Sam)