CS109: Probability for Computer Scientists

While the initial foundations of computer science began in the world of discrete mathematics (after all, modern computers are digital in nature), recent years have seen a surge in the use of probability as a tool for the analysis and development of new algorithms and systems. As a result, it is becoming increasingly important for budding computer scientists to understand probability theory, both to provide new perspectives on existing ideas and to help further advance the field in new ways.

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 particular probability distributions, properties of probabilities, and mathematical tools for analysing probabilities. Finally, the last third of the class will focus on data analysis and machine learning as a means for seeing direct applications of probability in this exciting and quickly growing subfield of computer science. This is going to be a great quarter, and we are looking forward to the chance to teach you.

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

Links
- Course: https://web.stanford.edu/class/cs109/
- Course Book: https://chrispiech.github.io/probabilityForComputerScientists/en/index.html
- Python for Probability: https://web.stanford.edu/class/archive/cs/cs109/cs109.1238/handouts/python.html

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