UW CSE 312: Foundations of Computing II
CSE 312 is the Allen School's discrete probability course and the sequel to CSE 311: counting and combinatorics, discrete and continuous random variables, expectation and variance, and applications of randomness to computing. It's the probabilistic foundation for machine learning, algorithms, and most quantitative upper-division CSE work.
Fennie is independent and not affiliated with University of Washington. This is an unofficial study guide.
What makes it hard
The course pivots from the proof-writing of CSE 311 to probabilistic reasoning, and the two skills feel unrelated. Setting up the right counting argument or recognizing which distribution a problem describes is where students stall. The math is rarely the hard part, the modeling is. Linearity of expectation problems in particular look trivial and routinely sink exam scores.
What you'll cover
- • Combinatorics and counting
- • Discrete probability and conditioning
- • Random variables and expectation
- • Variance and common distributions
- • Continuous probability and densities
- • Tail bounds and applications to computing
The CSE 312 study guide
How to study for UW CSE 312, step by step.
- 1
Rebuild your counting toolkit first
Most CSE 312 probability errors are counting errors in disguise. Spend the opening weeks until permutations, combinations, and the inclusion-exclusion principle are automatic, since everything downstream multiplies on top of them.
- 2
Name the distribution before computing
For every problem, decide whether it's binomial, geometric, Poisson, or something else before touching the math. Recognizing the model is the tested skill; the formula follows once you've named it.
- 3
Drill linearity of expectation relentlessly
These problems look trivial and quietly wreck exam scores. Work a dozen indicator-variable problems until decomposing a complicated expectation into a sum of simple ones is reflexive.
- 4
Write the setup in words, then the math
Define your sample space and random variables explicitly before any calculation. Partial credit on CSE 312 lives in a clearly stated model, and skipping it is how clean computations end up answering the wrong question.
Today
Today's CSE 312 plan
What a Fennie Daily Plan looks like for CSE 312. Yours is built from your own syllabus and adapts every day to your deadlines and progress.
First plan free, no card required. Fennie is independent and unaffiliated with your school.
FAQ
Is CSE 312 hard?
It's a real adjustment from CSE 311: probabilistic modeling instead of proofs. The arithmetic is light, but setting up the right counting argument or naming the right distribution under exam pressure is where students lose points.
What's the prerequisite for CSE 312?
CSE 311 is the standard prerequisite, and comfort with the calculus sequence helps for the continuous-probability portion. Check current Allen School requirements, which evolve.
How do I study for CSE 312 exams?
Work problems by naming the distribution or counting technique before computing, and drill linearity-of-expectation problems specifically. They look easy and consistently cost points.
More UW courses
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