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  CSE 312Wi '17:  Foundations of Computing II
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 Schedule & Reading
 Midterm Review
 Final Review
Course Email/BBoard
Lecture Notes
 1:  Intro
 2:  Counting
 3:  Discrete Probability
 4:  Conditional Probability
 5:  Independence
 6:  Rand Vars; E[-] & Var[-]
 7:  Continuous RVs
   Table of the Normal CDF
 9:  Tail Bounds
   Tail Summary
 10:  Limit Theorems
   LLN & CLT Demos
 11:  Max Likelihood Estimators
 12:  Expectation Maximization
   EM Example (.xls)
Lecture Recordings
 R Quickstart
 LaTeX Quickstart

Lecture:  JHN 102 MWF 1:30- 2:20 
Section A:  EEB 045 Th 12:30- 1:20  Daniel Jones + Tzu-Ling (Ariel) Lin
Section B:  EEB 031 Th 1:30- 2:20  Alexander Tsun
Section C:  MGH 242 Th 2:30- 3:20  Varun Vijay Mahadevan
Section D:  MGH 231 Th 11:30- 12:20  Jonathan Lee
Office Hours Location Phone
Instructor:  Larry Ruzzo, ruzzocs  F 2:30- 3:30  CSE 554  543-6298
TAs:  Daniel Jones, dcjonescs  M 11:30- 12:30  CSE 218 
  Jonathan Lee, jlee27cs  M 5:30- 6:30  CSE 220 
  Tzu-Ling (Ariel) Lin, arielincs  M 2:30- 3:30  CSE 218 
  Varun Vijay Mahadevan, varun94cs  Tu 3:00- 4:00  CSE 203 
  Alexander Tsun, alextsuncs  Tu 10:00- 11:00  CSE 021 

Course Email: Staff announcements and general interest student/staff Q&A about homework, lectures, etc. The instructor and TAs are subscribed to this list. Enrolled students are as well, but probably should change their default subscription options. Messages are automatically archived. 

Discussion Board: Also feel free to use Catalyst GoPost to discuss homework, etc.

Catalog Description: Examines fundamentals of enumeration and discrete probability; applications of randomness to computing; polynomial-time versus NP; and NP-completeness.

Prerequisites: CSE 311; CSE 332, which may be taken concurrently

Credits: 4

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 and (4) some basic methods of statistics and their use in a computer science & engineering context.

Grading: Homework, Midterm, Final. Possibly some quizes, small programming assignments. Overall weights 55%, 15%, 30%, roughly.

Late Policy: Assignments are due at the start of lecture on the due date, either on paper or electronically. Late papers/e-turnin will be accepted (but penalized 25%) up to the start of the next lecture; not accepted thereafter, barring major emergencies.

Extra Credit: Assignments may include "extra credit" sections. These will enrich your understanding of the material, but at a low points per hour ratio. Do them for the glory, not the points, and don't start extra credit until the basics are complete.

Collaboration: Homeworks are all individual, not group, exercises. Discussing them with others is fine, even encouraged, but you must produce your own homework solutions. 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.



Introduction to Probability (2nd edition), Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2008(Available from U Book Store, Amazon, etc.)

Reference. (No direct use of this, but if you already own a copy, keep it for reference. Some students have said they like its coverage of counting (Chapter 5 and 7.5, 7.6) and discrete probability (Chapter 6). Editions other than the sixth are probably equally useful.):

Discrete Mathematics and Its Applications, (sixth edition) by Kenneth Rosen, McGraw-Hill, 2006. Errata. (Available from U Book Store, Amazon, etc.)

Portions of the CSE 312 Web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly credited. The CSE 312 Web: © 1993-2017, the Authors and the Department of Computer Science and Engineering, University of Washington.

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