CS109: Probability for Computer Scientists, Spring 2022

Announcements and Updates

Thu, Jun 12: Everyone! Due in no small part to the heroics of your CS109 CAs, your final exams have been graded and available for you to review by just visiting Gradescope. You can also read over my responses to this Ed post to get word on how everyone did and when regrade requests will be enabled!
Problem Set 6 is being graded as I type, and we'll be releasing Problem Set 6 grades and posting final grades to Axess in the coming days. Stay tuned for more end-of-quarter updates, coming soon!

This Week

Week 10

Mon, May 30

Memorial Day Holiday: No lecture

Tue, May 31

Section 9: Final Exam Review

handout solution

Wed, June 1

Lecture 28: Deep Learning, Concluding Remarks

slides annotated lecture
digit-classification-demo
deep-reinforcement-learning-demo

No assigned reading.

Fri, Jun 3

No lecture

PSet 6 In

Schedule

Week 1

Mon, Mar 28

Lecture 1: Introductions, Counting

slides lecture

Piech: Counting

Wed, Mar 30

Lecture 2: Permutations and Combinations

slides annotated lecture

PSet 1 Out

Piech: Combinatorics

Fri, Apr 1

Lecture 3: Axioms of Probability

slides annotated lecture
seredipity-demo

Ross: Ch 2.1-2.5, 2.7
Piech: Probability, Equally Likely Outcomes

Week 2

Mon, Apr 4

Lecture 4: Conditional Probability and Bayes

slides annotated lecture
bayes-demo

Ross: Ch 3-1-3.3
Piech: Conditional Probability, Law of Total Probability, Bayes Theorem

Tue, Apr 5

Section 1: Analytic Probability

handout solution

Wed, Apr 6

Lecture 5: Independence

slides annotated lecture

Ross: Ch 3.4-3.5
Piech: Independence

Fri, Apr 8

Lecture 6: Random Variables and Expectation

slides slides lecture

PSet 1 In, PSet 2 Out

Ross: Ch 4.1-4.4
Piech: Random Variables, Probability Mass Functions

Week 3

Mon, Apr 11

Lecture 7: Fairness, Variance, Bernoulli, Binomial

fairness bernoulli annotated lecture galton-demo

Ross: Ch 4.5-4.6
Piech: Variance, Bernoulli, Binomial

Tue, Apr 12

Section 2: Random Variables and Expectation

handout solution

Wed, Apr 13

Lecture 8: Poisson and Approximations

slides annotated lecture

Ross: 4.7-4.10
Piech: Poisson

Fri, Apr 15

Lecture 9: Continuous Random Variables

slides annotated lecture

Ross: Ch 5.1-5.3, 5.5
Piech: Continuous RVs

Week 4

Mon, Apr 18

Lecture 10: The Normal Distribution

slides annotated lecture
elo-demo

PSet 2 In, PSet 3 Out

Ross: Ch 5.4,
Piech: Gaussian Distributions, Binomial Approximations

Tue, Apr 19

Section 3: Discrete and Continuous Random Variables

handout solution

Wed, Apr 20

Lecture 11: Joint Distributions

slides annotated lecture

Quiz 1 Out

Ross: Ch 6.1,
Piech: Joint Probability, Multinomials, Federalist Papers

Fri, Apr 22

Lecture 12: Independent Random Variables

slides annotated lecture

Quiz 1 In

Ross: Ch 6.2-6.3,
Piech: Adding Random Variables

Week 5

Mon, Apr 25

Lecture 13: Joint RV Statistics

slides annotated lecture

Ross: Ch 6.4-6.5,
Piech: Correlation

Tue, Apr 26

Section 4: Normal Distributions and Joint RV Statistics

handout solution

Wed, Apr 27

Lecture 14: Conditional Expectation

slides annotated lecture

Ross: Ch 7.1-7.2,
Piech: No assigned reading

Fri, Apr 29

Lecture 15: General Inference

slides annotated lecture sampling-demo

PSet 3 In, PSet 4 Out, CS109 Contest Out

Ross: No assigned reading,
Piech: Inference

Week 6

Mon, May 2

Lecture 16: Continuous Joint Distributions

slides annotated lecture

Ross: Ch 6.1,
Piech: no assigned reading

Tue, May 3

Section 5: Conditional Expectation

handout solution

Wed, May 4

Lecture 17: Continuous Joint Distributions II

slides annotated lecture

Ross: Ch 7.3-7.4,
Piech: no assigned reading

Fri, May 6

Lecture 18: Central Limit Theorem

slides annotated lecture
clt-demo sampling-demo

Ross: Ch 8.3,
Piech: Central Limit Theorem

Week 7

Mon, May 9

Lecture 19: Sampling/Bootstrapping

slides annotated lecture bootstrap-demo

PSet 4 In, PSet 5 Out,

Ross: No assigned reading,
Piech: Sampling, Bootstrapping

Tue, May 10

Section 6: Continuous Joint Random Variables

handout solution

Wed, May 11

Lecture 20: Sampling/Bootstrapping Wrap-Up

no new slides lecture

Quiz 2 Out

Ross: No assigned reading,
Piech: No additional reading beyond this past Monday's

Fri, May 13

Lecture 21: Parameters and MLE

slides annotated lecture 1
lecture 2 mle-demo

Quiz 2 In

Ross: No assigned reading,
Piech: MLE, MLE Normal, MLE Mixture

Week 8

Mon, May 16

Lecture 22: Medical AI, Ethics, and Calculated Risk, Beta

medical ai slides beta slides
annotated lecture

Ross: Ch 5.6.1-5.6.4, 7.5-7.6,
Piech: Beta

Tue, May 17

Section 7: Boostrapping, MLE, and Beta

handout solution

Wed, May 18

Lecture 23: Maximum a Posteriori

slides annotated lecture

Ross: No assigned reading.,
Piech: Maximum a Posteriori

Fri, May 20

Lecture 24: Naive Bayes

slides annotated lecture

PSet 5 In, PSet 6 Out

Ross: No assigned reading.,
Piech: Machine Learning, Naive Bayes

Week 9

Mon, May 23

Lecture 25: Linear Regression, Gradient Ascent

slides annotated lecture gradient-ascent-demo

Ross: No assigned reading.,
Piech: No assigned reading.

Tue, May 24

Section 8: Naive Bayes

handout solution recording

Wed, May 25

Lecture 26: Logistic Regression, Take I

slides annotated lecture

Ross: No assigned reading., Piech: Logistic Regression

Fri, May 27

Lecture 27: Logistic Regession Wrap, Ethics of Machine Learning

slides lecture

Ross: No assigned reading.,
Piech: No assigned reading.

Week 10

Mon, May 30

Memorial Day Holiday: No lecture

Tue, May 31

Section 9: Final Exam Review

handout solution

Wed, June 1

Lecture 28: Deep Learning, Concluding Remarks

slides annotated lecture
digit-classification-demo
deep-reinforcement-learning-demo

No assigned reading.

Fri, Jun 3

No lecture

PSet 6 In

Note that all lectures and assignment deadlines are subject to change.

Our CS109 website imitates that used by University of Washington's CSE373, Spring 2019.