Poisson distribution

Proofs

Modular arithmetic

RSA (cryptosystem)

Countability

Conditional probability

Random variable

Continuous Probability

Fermat’s Little Theorem

Secret Sharing

Counting

Combinations of Events

Total Expectation

Central Limit Theorem

Stable Matching

Expectation

Propositional calculus (Propositional logic)

Graphs

Chinese Remainder Theorem

Error Correcting Codes

Discrete Probability

Bayes' theorem

Variance

Concentration Inequalities

Induction

Euclid

Polynomials

Computability

Independence (probability theory)

Distributions

Joint and Conditional PMFs

Gaussian Distribution

CS 70: Discrete Mathematics and Probability Theory UC Berkeley Fall 2022 CS 70 presents key ideas from discrete mathematics and probability theory with emphasis on their application in Electrical Engineering and Computer Sciences. It addresses a variety of topics such as logic, induction, modular arithmetic, and probability. Sophomore mathematical maturity and programming experience equivalent to an Advanced Placement Computer Science A exam are prerequisites. Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference. The goal of this course is to introduce students to ideas and techniques from discrete mathematics that are widely used in Electrical Engineering and Computer Sciences. The course aims to present these ideas "in action"; each one will be geared towards a specific significant application. Thus, students will see the purpose of the techniques at the same time as learning about them. Sophomore mathematical maturity, and programming experience equivalent to that gained with a score of 3 or above on the Advanced Placement Computer Science A exam. There is no textbook for this class. Instead, there is a set of comprehensive lecture notes posted on [the front page](https://www.eecs70.org/) for each lecture. Make sure you revisit the notes after every lecture. Each note may be covered in one or more lectures.

Distinguished Alum Megan says, “When I took the course, I studied the notes until I was able to comfortably reproduce all of the proofs.

CS 70: Discrete Mathematics and Probability Theory

Mathematical Foundations

Combinatorics

Bernoulli distribution

Binomial distribution

Continuous Random Variables

Normal distribution

Joint probability distribution

Statistical inference

Beta distribution

P-Value

Algorithmic Analysis

Maximum Likelihood Estimation (MLE)

Maximum A Posteriori (MAP)

Naive Bayes

Logistic Regression

Bootstrapping

CS 109 Probability for Computer Scientists Stanford University Spring 2023 This course offers a thorough understanding of probability theory and its applications in data analysis and machine learning. Prerequisites include CS103, CS106B, and Math 51 or equivalent courses. CS109: Probability for Computer Scientists starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many essential concepts in probability theory, including common probability distributions, properties of probabilities, and mathematical tools for analyzing probabilities. The last third of the class will focus on data analysis and machine learning as direct applications of probability we've learned in the weeks prior. Read more here to learn what CS109 is all about. This is going to be a great quarter and we are looking forward to the chance to teach you!! After you're finished with CS109, we hope you'll have achieved the following learning goals:

- Reason about situations using probabilities, expectation, and variance.
- Feel comfortable learning about new probability concepts beyond the scope of this class (e.g., through one's own research, studies, or interests).
- Write programs to simulate random experiments and to test experiment hypotheses.
- Understand and implement simple machine learning algorithms like Naive Bayes and Logistic Regression.

## Course Topics

Here are the broad strokes of the course (in approximate order). More information is available on our Schedule page. We cover a very broad set of topics so that you are equipped with the probability and statistics you will see in your future CS studies!

- Counting and probability fundamentals
- Single-dimensional random variables
- Probabilistic models
- Uncertainty theory
- Parameter estimation
- Introduction to machine learning The prerequisites for this course are CS103, CS106B, and Math 51 (or equivalent courses). Probability involves a fair bit of mathematics (set theory, calculus, and familiarity with linear algebra), and we'll be considering several applications of probability in CS that require familiarity with algorithms and data structures covered in CS106B. Here is a quick rundown of some of the mathematical tools from CS103 and Math 51 that we'll be using in this class: multivariate calculus (integration and differentiation), linear algebra (basic operations on vectors and matrices), an understanding of the basics of set theory (subsets, complements, unions, intersections, cardinality, etc.), and familiarity with basic proof techniques. We'll also do combinatorics in the class, but we'll be covering a fair bit of that material ourselves in the first week. Past students have managed to take CS106B concurrently with CS109 and have done just fine. CS103 is the pre-requisite that we rely on the least. See our FAQ for more information. ## Optional Textbook

Sheldon Ross, *A First Course in Probability* (10th Ed.), Pearson Prentice Hall, 2018.

This is an optional textbook, meaning that the text is not required material, but students may find Ross offers a different and useful perspective on the important concepts of the class. Suggested, optional reading assignments from the textbook (10th Ed.) are in the schedule on the course website. The 8th, 9th, and 10th editions of the textbook are all fine for this class.

*Borrowing the textbook online*: HathiTrust, a library archive of which Stanford is a member, has granted the university online access to the 8th edition (2010) for the duration of the Fall quarter. The "check out" system works similarly to print reserves: A user can check out the book an hour at a time as long as they are actively using it. Access guidelines are on the [HathiTrust How To Use It webpage](https://www.hathitrust.org/ETAS-How-To). Once you're logged in, the book is at [this link](https://catalog.hathitrust.org/Record/006904347).

All students should retain receipts for books and other course-related expenses, as these may be qualified educational expenses for tax purposes. If you are an undergraduate receiving financial aid, you may be eligible for additional financial aid for required books and course materials if these expenses exceed the aid amount in your award letter. For more information, review your award letter or visit the [Student Budget website](https://financialaid.stanford.edu/undergrad/budget/index.html).

CS 109 Probability for Computer Scientists

Poisson distribution

2 courses cover this concept

CS 70: Discrete Mathematics and Probability Theory

CS 109 Probability for Computer Scientists