CSE 312: Foundations of Computing II, Fall, 2016

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Important Announcements

Schedule and Handouts

Date Topic Reading
Wednesday, September 28 Administrivia and counting (updated 9/30) [Rosen 5.1-5.5], [LLM, chap 15], [BT, 1.6]
Friday, September 30 More counting (also see 9/28 slides)
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]
Thursday, October 6 Practice on discrete probability
Friday, October 7 More equally likely outcomes, intro cond. prob [BT, 1.5]
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]
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)
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
Thursday, October 27 Notes on Naive Bayes classification
Friday, October 29 Naive Bayes Classifiers
Monday, October 31 More zoo
Wednesday, Nov 2 Hypergeometric distn (see 10/31 slides) + Joint distributions note on hypergeometric distn
Friday, November 4 Conditional expectation
Monday, November 7 Randomized quicksort Notes from CMU
Wednesday, November 9 Midterm
Monday, November 14 Continuous r.v.'s. (notes + slides) Notes from Berkeley and [BT, 3.1-3.3]
Wednesday, November 16 Continuous r.v.'s. (see 11/14 slides)
Friday, November 18 Continue continuous (slides + notes) [BT, 3.4-3.6]
Monday, November 21 Distinct Elements
Wednesday, November 23 Distinct Elts cont. (see Monday's handout)
Monday, November 28 Tail Bounds and Central Limit Theorem Berkeley notes and see page 11
Wednesday, November 30 Learning and Maximum likelihood estimation [BT, 9.1] (in actual book) and an introduction
Friday, December 2 EM: slides + notes Notes to help with homework
Monday, December 5 More EM
Wednesday, December 7 Pagerank
Friday, December 9 Practice for final

Course Information

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

All Office Hours

Wednesday office hours Thursday office hours Friday office hours
2:30-3:30pm: Varun, CSE 306

5-6pm Jonathan, CSE 218

10:30-11:30am: Alex, CSE 218

4-5pm: Anna, CSE 594

6-7pm: Sai, CSE 218

11-12pm: Justin, CSE 218

2-3pm: Jonathan, CSE 306

Course evaluation and grading:


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.

Send email to entire class.

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.