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)
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