**calendar **
| **important announcements]**
| **GoPost discussion board**
| **course information**
| **
anonymous feedback**
| **prior incarnations of course**
| **acknowledgements**

- Here is the calendar for the course. Almost all information (such as lecture materials and assignments) is/will be linked from the calendar.
- To submit daily problems, you'll need to log in to the Catalyst Dropbox.
- Videos of lectures: from the fall 2013 incarnation in the course (bottom left of page) and from MIT (lectures 16 - 25)
- Quick guide for getting started with latex, a typesetting system that is very useful for typesetting mathematics.

Lectures time and place: MWF 9:30-10:20am, in GWN 301

Sections time and place: AC: Thursday 12:30 -- 1:20 in MGH 234; AA: Thursday 1:30 -- 2:20 in MGH 271; AB: Thursday 2:30 -- 3:20 in EEB 031

**Instructor:** Anna Karlin,
CSE 594, tel. 543 9344

**Office hours:** Thursdays: 9:30-11am, CSE 594, and
by appointment -- just send email.

**Teaching assistants:** Kira Goldner, Phillip Huang, Stephen Jonany, and
Gunnar Onarheim

**Office hours:**
Tuesdays 4:30-5:30pm, Wednesdays 4:30-5:30pm and Thursdays 4:00-5:00pm in CSE 218 and by appointment.

**Course evaluation and grading: **

- Rough breakdown: Daily problems (5%), weekly problem sets (altogether 20%), two midterms (20% each) and final (35%).
- Daily problems will be graded as credit/no-credit, with credit received for a credible effort.
- We will carefully grade only two problems per homework set (but we're not going to tell you which ones in advance). The rest of the problems will receive full credit for any credible effort.
**Late homework and daily problems**will not be accepted, barring major emergencies. You have a pass on turning daily problems in four times during the quarter, and we will drop your lowest homework score.

**Textbooks:**

- [BT] (optional)
Introduction to Probability (2nd edition), Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2008 (Available from U Book Store, Amazon, etc.)
**1st edition, free online** - [LLM] (free online) Mathematics for Computer Science, Lehman, Leighton and Meyer. (Chapters 15, 17-20).
- [DBC] (free online) OpenIntro Statistics, Dietz, Barr and Cetinkaya-Rundel.
- [R] (optional) Kenneth H. Rosen, Discrete Mathematics and Its Applications, Sixth Edition, McGraw-Hill, 2007. 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)).

**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.

The mailing list (cse312a_au14@uw.edu) 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.

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.