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Stanford CS109: Probability for Computer Scientists: study plan and how long it takes

The Stanford Online playlist is 29 videos and 35.4 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.

29 videos · 35.4 hours · Stanford Online

Stanford CS109 (2022), taught by Chris Piech, teaches probability from a computer scientist's point of view: counting and combinatorics, conditional probability and independence, random variables and distributions, joint distributions, inference, bootstrapping, maximum likelihood, Naive Bayes, logistic regression and the probability behind deep learning.

It's one of the most engaging probability courses online, and a strong foundation for machine learning.

Who it's for: CS students and programmers who want a rigorous, applied probability course before studying ML.

How long does it take?

The full playlist is 35.4 hours across 29 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:

Days to finish the playlist by daily watch time and playback speed
Daily timeAt 1xAt 1.5xAt 2x
30 min a day71 days48 days36 days
1 h a day36 days24 days18 days
2 h a day18 days12 days9 days

Make your own schedule

The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.

Open on YouTube Track watched videos

Week-by-week schedule

A sample plan at 1 hour a day and normal speed, using whole videos so none is split across days. That's 5 weeks in total. For a different pace, start date or speed, use the planner above.

Week 1Videos 1–7 · 8h 33m

  1. Day 1 · 1h 14m

    • 1Stanford CS109 Probability for Computer Scientists I Counting I 2022 I Lecture 11h 14m

  2. Day 2 · 1h 9m

    • 2Stanford CS109 Probability for Computer Scientists I Combinatorics I 2022 I Lecture 21h 9m

  3. Day 3 · 1h 15m

    • 3Stanford CS109 Probability for Computer Scientists I What is Probability? I 2022 I Lecture 31h 15m

  4. Day 4 · 1h 15m

    • 4Stanford CS109 I Conditional Probability and Bayes I 2022 I Lecture 41h 15m

  5. Day 5 · 1h 18m

    • 5Stanford CS109 Probability for Computer Scientists I Independence I 2022 I Lecture 51h 18m

  6. Day 6 · 1h 8m

    • 6Stanford CS109 I Random Variables and Expectation I 2022 I Lecture 61h 8m

  7. Day 7 · 1h 14m

    • 7Stanford CS109 Probability for Computer Scientists I Variance Bernoulli Binomial I 2022 I Lecture 71h 14m
Week 2Videos 8–14 · 8h 44m

  1. Day 8 · 1h 13m

    • 8Stanford CS109 Probability for Computer Scientists I Poisson I 2022 I Lecture 81h 13m

  2. Day 9 · 1h 16m

    • 9Stanford CS109 Probability for Computer Scientists I Continuous Random Variables I 2022 I Lecture 91h 16m

  3. Day 10 · 1h 15m

    • 10Stanford CS109 Probability for Computer Scientists I Normal Distribution I 2022 I Lecture 101h 15m

  4. Day 11 · 1h 12m

    • 11Stanford CS109 Probability for Computer Scientists I Joint Distributions I 2022 I Lecture 111h 12m

  5. Day 12 · 1h 21m

    • 12Stanford CS109 Probability for Computer Scientists I Inference I 2022 I Lecture 121h 21m

  6. Day 13 · 1h 11m

    • 13Stanford CS109 Probability for Computer Scientists I Inference II I 2022 I Lecture 131h 11m

  7. Day 14 · 1h 17m

    • 14Stanford CS109 Probability for Computer Scientists I Modelling I 2022 I Lecture 141h 17m
Week 3Videos 15–21 · 8h 24m

  1. Day 15 · 1h 16m

    • 15Stanford CS109 Probability for Computer Scientists I General Inference I 2022 I Lecture 151h 16m

  2. Day 16 · 1h 8m

    • 16Stanford CS109 Probability for Computer Scientists I Beta I 2022 I Lecture 161h 8m

  3. Day 17 · 1h 17m

    • 17Stanford CS109 Probability for Computer Scientists I Adding Random Variables I 2022 I Lecture 171h 17m

  4. Day 18 · 49m

    • 18Stanford CS109 I Central Limit Theorem I 2022 I Lecture 1849m

  5. Day 19 · 1h 18m

    • 19Stanford CS109 Probability for Computer Scientists I Bootstraping and P-Values I 2022 I Lecture 191h 18m

  6. Day 20 · 1h 18m

    • 20Stanford CS109 I Algorithmic Analysis I 2022 I Lecture 201h 18m

  7. Day 21 · 1h 18m

    • 21Stanford CS109 Probability for Computer Scientists I M.L.E. I 2022 I Lecture 211h 18m
Week 4Videos 22–28 · 8h 54m

  1. Day 22 · 1h 18m

    • 22Stanford CS109 Probability for Computer Scientists I M.A.P. I 2022 I Lecture 221h 18m

  2. Day 23 · 1h 18m

    • 23Stanford CS109 Probability for Computer Scientists I Naive Bayes I 2022 I Lecture 231h 18m

  3. Day 24 · 1h 19m

    • 24Stanford CS109 Probability for Computer Scientists I Logistic Regression I 2022 I Lecture 241h 19m

  4. Day 25 · 1h 21m

    • 25Stanford CS109 I Deep Learning I 2022 I Lecture 251h 21m

  5. Day 26 · 1h 10m

    • 26Stanford CS109 I Fairness I 2022 I Lecture 261h 10m

  6. Day 27 · 1h 21m

    • 27Stanford CS109 I Advanced Probability I 2022 I Lecture 271h 21m

  7. Day 28 · 1h 7m

    • 28Stanford CS109 I Future of Probability I 2022 I Lecture 281h 7m
Week 5Video 29 · 51m

  1. Day 29 · 51m

    • 29Stanford CS109 Probability for Computer Scientists I Counting I 2022 I Lecture 2951m

Tips for finishing

  • Plan two or three lectures a week and do the problem sets from the course site.
  • The MLE and Naive Bayes lectures connect directly to machine learning. If ML is your goal, don't skip them.

Frequently asked questions

How long does it take to finish Stanford CS109: Probability for Computer Scientists?

The playlist has 29 videos and 35.4 hours of content. At 1 hour a day that's about 36 days at normal speed, or 24 days at 1.5x. At 2 hours a day it's about 18 days.

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