CSE 312: Foundations of Computing II, Fall, 2018
| important announcements]
| discussion board
| office hours
| general course information
| prior incarnations of course
| acknowledgements
Important Announcements
Schedule and Handouts
Date |
Topic |
Reading |
Wed, Sept 26 |
Administrivia and counting
|
Berkeley notes
[Rosen 5.1-5.5], [LLM, chap 15], [BT, 1.6] |
Fri, Sept 28 |
More counting
|
|
Mon, Oct 1 |
Finish counting (see Sept 28 slides)
Intro discrete probability
(See Berkeley intro probability slides) |
Berkeley notes
[Rosen, chap 6][BT, chap 1, especially 1.3-1.4] [LLM, chap 17] |
Wed, Oct 3 |
Events and practice with probability
|
|
Fri, Oct 3 |
Conditional probability
(See Berkeley conditional probability slides) |
Berkeley notes, [BT, chap 1.5, 2.1-2.3] [LLM, chap 17] |
Mon, Oct 8 |
More conditional probability, independence
|
See Friday reading + [BT, 2.4] |
Wed, Oct 10 |
Bayes Theorem
| |
Friday, Oct 12 |
More on independence
|
Berkeley notes |
Monday, Oct 15 |
More independence, Naive Bayes Intro Random Variables |
[Rosen 6.2, 6.4][BT, Chap 2], [LLM, chap 18] |
Wed, Oct 17 |
Random vars and expectation | ,
Berkeley notes
|
Fri, Oct 19 |
Linearity of expectation | ,
|
Mon, Oct 22 |
Variance |
Berkeley notes |
Wed, Oct 24 |
More variance, independence of r.vs + start zoo |
Berkeley notes |
Fri, Oct 26 |
More r.v.s, conditional expectation |
[LLM, chap 18]
especially Sections 18.4.5-18.4.6 |
Mon, Oct 29 |
More Poisson + LTE |
|
Wed, Oct 31 |
Randomized quicksort |
Notes from CMU
|
Fri, Nov 2 |
Stream processing and heavy hitters (only slides 1-10) |
Heavy hitter notes from Stanford
+ Markov's Inequality |
Mon, Nov 5 |
Heavy hitters |
Princeton notes on universal hashing (sections 2-4) |
Wed, Nov 7 |
Joint distributions plus some slides |
[BT] 2.5 |
Fri, Nov 9 |
midterm |
|
Wed, Nov 14 |
Continuous random variables (plus slides) |
Berkeley notes |
Fri, Nov 16 |
Continue continuous |
[BT, 3.4-3.6] |
Mon, Nov 19 |
Tail bounds and limit theorems
(Some pictures)
|
Berkeley Notes (Chebychev's Inequality + LLN)
+ Berkeley Notes (CLT) |
Wed, Nov 21 |
Practice with continuous r.v.s |
|
Mon, Nov 26 |
CLT addendum +
more continuous practice +
intro MLE |
[BT, 9.1] (in actual book) and an introduction |
Wed, Nov 28 |
More MLE |
Some notes from Penn (we didn't cover method of moments) |
Fri, Nov 30 |
Finish MLE + start distinct elements |
|
Mon, Dec 3 |
Continue distinct elements |
|
Course Information
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
All Office Hours (in Allen Center)
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.
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.
acknowledgements
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.