| **important announcements]**
| **discussion board**
| **office hours**
| **general course information**
| **prior incarnations of course**
| **acknowledgements**

**Our final will be in Paccar Hall Room 192 on Wednesday, December 12, 8:30-10:20 AM**You can bring one sheet of notes to the final.

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

Sections time and place:
AA: Thursday 12:30 -- 1:20 in MGH 234; AB: Thursday 1:30 -- 2:20 in MGH 287; AC: Thursday 2:30 -- 3:20 in MGH 228; AD: Thursday 11:30-12:20 in JHN 175

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

**Office hours:** Thursdays: 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 | Thursday office hours | Friday office hours |
---|---|---|---|

3:00-4:00pm: Anna, CSE 594
4:00-5:00pm: Cheng, 3rd floor breakout |
1:30-2:30pm: Sierra, CSE 306
5:00-6:00pm: Andrew, 4th floor breakout 6:00-7:00pm: Nathan, CSE 306 |
9:00-10:00am: Anna, CSE 594 |
2:00-3:00pm: Kushal, 2nd floor breakout
3:00-4:00pm: Boyan, 5th floor breakout |

**Course evaluation and grading: **

- Approximate breakdown: Weekly problem sets (altogether 35%), midterm (25%) and final (40%).
**Late homework**will not be accepted, barring major emergencies.

**Textbooks:**

- A major resource will be Notes 12-26 from Berkeley CS 70 (scroll down and then expand "Notes").
- [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)).
- [Ross] (optional) Sheldon Ross, Introduction to Probability Models, Academic Press. (earlier editions are fine).

**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 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 extensively on materials from CS 70 at Berkeley,
"Mathematics for Computer Science" at MIT, and
"Great
Theoretical Ideas in Computer Science" at CMU.