EXAMS
Midterm
Monday, February 10
7:00PM9:00PM
Cubberley Auditorium
Final
Wednesday, March 18
3:30PM6:30PM
Canvas/Gradescope
TEACHING TEAM
cs109 @ cs.stanford.eduINSTRUCTOR
David VarodayanANNOUNCEMENTS
We received 39 contest entries this quarter and were impressed by the creativity and probability knowhow the entries showed!
Winner: Sauren Khosla, Recidivism Risk: Algorithmic Prediction and Racial Bias
Runner Up: Riley Noland, Everyday I'm Shuffling: A Probabilistic Analysis of Card Shuffling
Update 3/2/2020: We have created a Pset 6 Python Guide in case you are having trouble running the starter code in a terminal. We have also updated the starter code. See the pset 6 webpage linked below for more information.
The final problem set is now available. You will implement two machine learning algorithms, Naive Bayes and Logistic Regression. You will then use these algorithms to make predictions on heart tomography, Netflix movies and ancestry.
The penultimate problem set is now available! This problem set has only 8 questions. 

The CLT says that if $Y$ is the sum of $n$ iid random variables (which all have expectation $\mu$ and variance $\sigma^2$) then:
The proof is beyond the scope of the class. A friendly CS109 student from a few quarters ago (Sophia Furfine) made a video of the proof in case you are curious!
This quarter we are going to hold the fifth Stanford Probability for Computer Scientists Contest. The contest is completely optional. See the contest handout for more details.
We're interested to know what you think of CS109 so far. We invite you to fill out an anonymous feedback form here: https://forms.gle/6JC6a4oyrH5hEGTy7. We'll keep the form open through Wednesday night, February 12.
Problem Set #4 has been released! It has you predict users based on biometric keystrokes. The pset clearly delineates the questions that you should do before the midterm.
The CS109 midterm is coming up: it is Monday, February 10, 7:00PM9:00PM, in Cubberley Auditorium. The midterm is a closed book, closed calculator/computer exam; you are, however, allowed to bring three 8.5" x 11" pages (front and back) of notes in the exam, formatted in any way you like. The last page of the exam will be a Stanford Normal Table, in case you need it.
The midterm will cover material up to and including lecture 11, which includes problem sets 1 to 3 and part of problem set 4 (which part will be clearly marked).
The best way to study is by working through the practice exams and section problems.
Review session: Emma, one of our TAs, will be hosting a review session Saturday, February 8, 3:00PM5:00PM in Sapp Center for Teaching & Learning (STLC) 111. This session will not be recorded, but all materials will be posted on the exam practice website afterwards.
Alternate midterm assignments (and OAE) have been made; you should have received an email with your specific arrangements. If you requested an alternate midterm and have not received your information, please email Alex (alextsun@).
Problem Set #3 has been released! It uses real probability density functions from the IPCC Climate Change report, and has you analyze a bloom filter (a probabilistic datastructure).
Problem Set #2 has been released! Here is a Latex template for pset 2.
Update (1/12/20): Session slides and last quarter's recording and slides are now up!
For those of you interested in learning/reviewing Python 3, Julie will be giving a Python tutorial on Friday, January 10, 3:304:20pm in 420041. We will try our best to record this session. We recommend looking at the Colab notebook beforehand. If you need help getting set up, this Piazza post should be a good starting point.
Update (1/13/20): Section assignments have been released. If you did not receive an email, please contact the staff mailing list. For any other section scheduling questions, please fill out the Late & Swap form ASAP. Section starts this week.
Please sign up for section by filling out this form: https://forms.gle/bZ5RhC2gq5g7NLYo9.
Once a week you are going to meet in a small group section. We are going to find the best weekly time for everyone. Section signups will close on Saturday, January 11 at 11:59pm. Preferences are not first come first serve. For more information, visit the Section Attendance page.
Problem Set #1 has been released! It is due next Friday, January 17 at 1:00pm. Submission will be via Gradescope with entry code MV66N5. Office hours will start tomorrow (Thursday), and the office hours calendar will have times and locations.
We have synced the class roster with Gradescope, so your Stanford email is likely to be registered already. If not, you can join the class by going to https://gradescope.com/ and clicking the button in the top right marked "Sign Up." Select "Student," then enter the entry code, your full name, email address, and 8digit student id.
You are encouraged to write up your problem sets using LaTeX. Templates for each Problem Set are located on their respective webpage. See this intro to LaTeX, and the LaTeX code used to generate it. Though you may install LaTeX, it is often much easier to use an online LaTeX editor. A great option is: overleaf.com.
Welcome to CS109! We are looking forward to a fun quarter. Class starts Monday, January 6, at 1:30pm in 420040.
We put together some handouts to help you understand where we are going to go in CS109 and how we plan to get there.
The Administrivia handout has details on course logistics. Read this to get a sense for what CS109 is going to entail.
The Course Schedule page shows you the topics that we are going to cover in CS109 and the corresponding readings. We will also post materials from lecture on the schedule page.
The Staff / Office Hours page has contact information for TAs and the office hour calendar. Office hours will start Thursday, January 9.
Once the quarter starts, you will need to sign up for a weekly 50minute discussion section. Details on how to sign up for section will be provided during the first week of class.
Week  Monday  Wednesday  Friday 

1

Jan 8 2: Permutations and Combinations
Read: Ch 1.31.6

Jan 10 3: Axioms of ProbabilityRead: Ch 2.12.5, 2.7 

2

Jan 13 4: Conditional Probability and BayesRead: Ch 3.13.3 
Jan 17 6: Random Variables and Expectation
Read: Ch 4.14.4


3

Jan 20 Martin Luther King Jr. DayNo Class 
Jan 22 7: Variance, Bernoulli, BinomialRead: Ch 4.54.6 

4

Jan 27 9: Continuous Random Variables
Read: Ch 5.15.3, 5.5

Jan 29 10: The Normal DistributionRead: Ch 5.4 
Jan 31 11: Joint DistributionsRead: Ch 6.1 
5

Feb 5 13: Independent Random Variables
Read: Ch 6.26.3


6

Feb 10 15: Correlation and Covariance
Read: Ch 7.37.4


7

Feb 17 Presidents DayNo Class 
Feb 19 18: Central Limit Theorem
Read: Ch 8.3

Feb 21 19: Sampling/BootstrappingRead: Lecture Notes 
8

Feb 28 22: Gradient Ascent
Read: Lecture Notes


9


10

Mar 13 28: Beyond CS109
Last day to submit assignments
