# Computational Probability and Statisitcs

## Week 14, April 24Apr 20, 2017 LINK

This is the final week of the course, and much of this week will be devoted to the review for the final exam.

This assignment, due by Tuesday, April 25, begins your preparation for the final:

The following assignment, also mandatory, is due before the quiz and contains the discussion from last week’s lecture:

Solution to question 4, homework 8:

## Week 13, April 17Apr 15, 2017 LINK

Homework, Part 1 (submit your solutions on April 20):

Homework, Part 2, Exercises with solutions. Quiz questions may be similar to these:

Slides:

## Weeks 9, 10 (March 20 - April 1)Mar 20, 2017 LINK

☞ HOMEWORK 6 is due on Thursday, March 30.

Exercises with solutions:

Slides:

• Notes: Chapter 3, General Random Variables. Sections 3.1 - 3.5 (pp 1 - 39).

Recitation starter files:

## Midterm to be held on Thursday, March 9Mar 5, 2017 LINK

The midterm will be held in class on Thursday, March 9.

The midterm will include (and will be limited to) all the material covered in class so far. In particular, you should be thoroughly comfortable with the questions that have been given on the quizzes or assigned for homework so far.

Moreover, you are strongly encouraged to practice for the exam by solving the following “extra practice problems”:

(It should not come as a surprise if a problem similar to any one of these “extra practice” problems shows up on the midterm.)

Good luck!

## Week 7, February 27Feb 24, 2017 LINK

Homework:

• Sections 2.3 - 2.6 from the notes , excluding the material related to the Poisson distribution (for now), must be completed by March 6 and begun as soon as possible.

Short exercises with solutions:

Slides

## Week 6, February 20Feb 18, 2017 LINK

- Questions 1 - 3 must be completed before the quiz on Thursday, Feb 23.
- The remaining problems must be completed before Thursday, March 2.

Homework:

Recitation Starter file:

Slides

## Snow Storm Related AnnouncementFeb 9, 2017 LINK

The homework has been updated. The problems that were to be handed in on February 9 may now be handed in on February 16. Together with these problems, please also hand in three additional problems on February 16. You will find them here:

## Week 4 MaterialFeb 4, 2017 LINK

• Lecture notes on elemental combinatorics, binomial coefficients.
• Lecture video - the counting principle, permutations, combinations, binomial coefficients.

## Week 3 MaterialJan 31, 2017 LINK

☞ HOMEWORK 3 is due on Thursday, February 9.
• Lecture video - examples of the Bayes' rule, independence of events.

Recitation 3 files:

Time permitting, we may begin the discussion of next week’s material. Notes can be found here:

More extra-practice problems with answers have been added to the problem pool:

## Week 2 MaterialJan 24, 2017 LINK

• Lecture notes on conditional probability, total probability and the Bayes rule.
• Lecture Video on total probability and the Bayes rule with the radar example.
☞ HOMEWORK 2 is due on Thursday, February 2.

In addition to problems assigned for homework, we will be posting “extra practice” problems with answers. We’ll be going over the solutions to some of these in class. Here is the first batch:

Recitation starter files for this week:

## Week 1 Material PostedJan 17, 2017 LINK

☞ HOMEWORK 1 is due on Thursday, January 26.

This week’s recitation covered an refresher/introduction to `python` (which included lists, dictionaries and comprehensions) as well as an introduction to ``` numpy ``` and plotting.

## Welcome to the Course!Jan 16, 2017 LINK

Welcome to your first CIS 2033 class! We’ll get to know each other, talk about what to expect from the course, and briefly discuss administrative matters. We’ll then jump straight into the course material.

Two features make this run of the course different from the previous versions:

• All programming assignments will be done in `python` (no need to worry if you have not programmed in python before: you'll pick it up as we go along).
• After the midterm, a significant portion of this course will be devoted to Bayesian statistics and some of its applications, and there will be a hands-on project based on this material.

Here is a link to the previous version (Fall ‘16) of the course.

Lecture slides, links to useful resources, homework and project assignments and other course related material will be posted here after each lecture.

Let’s have a great semester!

• Instructor
DAVID DOBOR
2dave at temple dot edu

• Office Hours
Held in SERC 327 on
M-W-F 3 - 3:50 PM or by appointment

• Class Meetings
Tuttelman 302
T-Th 2 - 3:20 AM

• Recitations
SERC 206
Th 11 - 12:50 AM

• Syllabus
Please note that this is a
python based course.

• Textbook
The textbook is optional
but strongly recommended.

• Notes