Adam Blank

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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!

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.

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:

- Visit the Counseling Center
- Visit Hall Health Mental Health Clinic
- Call SafeCampus at (206) 685-7233

Hopper

Ruffff!

Cat

Meow.

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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

Application: Passwords

Section: Final Review

Victory Lap