| important announcements]
| GoPost discussion board
| office hours
| general course information
| prior incarnations of course
| acknowledgements
Date | Topic | Reading | Homework | Section materials |
---|---|---|---|---|
Wednesday, September 28 | Administrivia and counting (updated 9/30) | [Rosen 5.1-5.5], [LLM, chap 15], [BT, 1.6] | Homework 1 and solutions | |
Friday, September 30 | More counting (also see 9/28 slides) | Section 1 worksheet and solutions | ||
Monday, October 3 | More counting | |||
Wednesday, October 5 | Intro discrete probability | [Rosen, chap 6][BT, chap 1, especially 1.3-1.4] [LLM, chap 17] | Homework 2 and solutions | Martin's notes |
Thursday, October 6 | Practice on discrete probability | Section 2 worksheet and solutions | ||
Friday, October 7 | More equally likely outcomes, intro cond. prob | [BT, 1.5] | Martin's notes | |
Monday, October 10 | Conditional Probability, Bayes Theorem | [BT, chap 1.5, 2.1-2.3] [LLM, chap 17] | ||
Wednesday, October 12 | Independence | [BT, 2.4] | Homework 3 and solutions | Section 3 worksheet and solutions |
Friday, October 14 | Problems (also see Wednesday's slides) | |||
Monday, October 17 | Intro Random Variables | [Rosen 6.2, 6.4][BT, Chap 2], [LLM, chap 18] | ||
Wednesday, October 19 | Random vars and expectation | Random variables (by Alex Tsun) | Homework 4 | Section 4 worksheet and solutions |
Friday, October 21 | Exp of fn of r.v. and linearity of expectation | |||
Monday, October 24 | Linearity of expectation (see 10/21 slides) + variance | |||
Wednesday, October 26 | Intro to zoo of r.v.s | Homework 5 | ||
Thursday, October 27 | Notes on Naive Bayes classification | Programming Project 1 and naivebayes.zip | Section 5 worksheet and solutions | |
Friday, April 29 | Naive Bayes Classifiers | Section 5 worksheet part 2 and solutions part 2 |
Lectures time and place: MWF 9:30-10:20am, in JHN 075
Sections time and place:
AA: Thursday 12:30 -- 1:20 in MGH 238; AB: Thursday 1:30 -- 2:20 in GLD 322; AC: Thursday 2:30 -- 3:20 in MEB 246
Instructor: Anna Karlin,
CSE 594, tel. 543 9344
Office hours: Tuesdays: 9:00-10am, CSE 594, and
by appointment -- just send email to Anna.
Teaching assistants: Send email to instructor + TAs
Monday office hours | Tuesday office hours | Wednesday office hours | Friday office hours |
---|---|---|---|
3:30-4:30pm: Alex, CSE 220
5:30-6:30pm: Justin, CSE 218 |
9-10am: Anna, CSE 594
5-6pm Saidutt, CSE 220 |
2:30-3:30pm: Varun, CSE 220
5-6pm Jonathan, CSE 218 |
2-3pm Jonathan, CSE 306 |
Course evaluation and grading:
Textbooks:
Learning Objectives:
Course goals include an appreciation and introductory understanding of (1) methods of counting and basic combinatorics, (2) the language of probability for expressing and analyzing randomness and uncertainty (3) properties of randomness and their application in designing and analyzing computational systems, (4) some basic methods of statistics and their use in a computer science & engineering context, and (5) introduction to inference.
Class mailing list:The mailing list is used to communicate important information that is relevant to all the students. If you are registered for the course, you should automatically be on the mailing list.
Academic Integrity and Collaboration:Homeworks are all individual, not group, exercises. Discussing them with others is fine, even encouraged, but you must produce your own homework solutions. Also, please include at the top of your homework a list of all students you discussed the homework with. We suggest you follow the "Gilligan's Island Rule": if you discuss the assignment with someone else, don't keep any notes (paper or electronic) from the discussion, then go watch 30+ minutes of TV (Gilligan's Island reruns especially recommended) before you continue work on the homework by yourself. You may not look at other people's written solutions to these problems, not in your friends' notes, not in the dorm files, not on the internet, ever. If in any doubt about whether your activities cross allowable boundaries, tell us before, not after, you turn in your assignment. See also the UW CSE Academic Misconduct Policy, and the links there.
Thanks to previous instructors of this course (James Lee, Larry Ruzzo,
Martin Tompa and Pedro Domingos) for the use of their slides and other
materials. (Some of these were in turn drawn from other sources.) We
have also drawn on materials from
"Mathematics for Computer Science" at MIT, and
"Great Theoretical Ideas in Computer Science" at CMU, from Edward Ionides at the University of Michigan, from an offering of CS 70 at Berkeley by Tse and Wagner,
and from an offering of 6.S080 at MIT by Daskalakis and Golland.