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Welcome to CSE 312! We have put the most important links at the top, categorized by what they're for. Please check them out!

## Getting Help

It is very important to us that you succeed in CSE 312. We provide many extra resources to help you. Adam and the TAs hold many office hours, we have a message board called , and we provide you with many practice handouts.

## Wellness and Accomodations

It is also very important to us that you maintain your mental wellness throughout the course. A few points are not worth losing sleep over. Everyone on the course staff is available to chat, and you can always attend office hours for a non-academic conversation if necessary. You can use the following resources if you find you need help beyond the course staff:
If you have a temporary health condition or permanent disability (either mental health or physical health related), you should contact DRS at uwdrs@uw.edu if you have not already. Additionally, if there is something we can do to make your experience better, please let us know.

## Course Staff

### Instructor CSE 444
(206) 616-0034

### Course Mascot

Hopper Ruffff!
Cat Meow.

## Schedule

• Combinatorics
• Discrete Probability
• Applications
• Continuous Probability
• Applications
• Machine Learning

#
Day
Topic
Homework
Combinatorics: Combinatorial Toolbox & Primitives
Combinatorics: Counting in Two Ways
Section: Combinatorial Toolbox & Counting in Two Ways
Combinatorics: The Binomial Theorem & Fancy Counting
Combinatorics: Fancy Counting
Discrete Probability: Axioms & Equally-Likely Outcomes
Section: More Combinatorics and Intro Probability
Discrete Probability: Conditional Probability & Law of Total Probability
Discrete Probability: Bayes' Theorem
Discrete Probability: Independence
Section: Conditional Probability
Application: Naive Bayes Classifier
Discrete Probability: Random Variables, Expectation, and Geometrics
Discrete Probability: Linearity of Expectation
Section: Random Variables and Linearity of Expectation
Projector Problems :((((((
Discrete Probability: Variance, Independent RVs, and Zoo of RVs
Application: Sampling and Shuffling
Section: Variance and Discrete Distributions
Discrete Probability: Poisson Distribution
Application: Randomized Algorithms
Application: Randomized Data Structures
Midterm Review
Continuous Probability: Introduction, RVs, Uniform Distribution
Section: Midterm Recap
Continuous Probability: More Distributions
Continuous Probability: Normal Distribution, Markov, Chebyshev
Continuous Probability: CLT and Law of Large Numbers
Section: Variance and Important Discrete Distributions
Continuous Probability: CLT Problems
Machine Learning: Maximum Likelihood Estimators
Machine Learning: More MLE and MAP Estimators
Section: Concentration Inequalities and MLE
Application: Error-Correcting Codes