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

- Videos of lectures: from the spring 2015 incarnation in the course (in reverse order; this is from a different instructor, so will be different in places) and from MIT (lectures 16 - 25)
- Quick guide for getting started with latex, a typesetting system that is very useful for typesetting mathematics.

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 |

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

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: **

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

**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)).
- [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 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.