CS109: Probability for Computer Scientists, Winter 2023
Note that all lectures and assignment deadlines are subject to change. Our CS109 website imitates that used by University of Washington's CSE373, Spring 2019.
CS109: Probability for Computer Scientists, Winter 2023 CS109: Probability for Computer Scientists, Winter 2023 Announcements and Updates Sun, Mar 12 : As promised, I've shared an extra coding problem that allows you to implement your own logistic regression classifier, much as you did with your Naive Bayes classifier on the official Problem Set 6. Note that this is just being posted for fun should you want to complete it to better understand logistic regression and gradient ascent. Please understand that this is not an extra credit opportunity. It is simply here for those who are curious. Fee
Explore this link on the map →saved by
related reading
- Probability & Statistics for Machine Learning & Data Science | Courseracoursera.org
- Poisson distribution - Wikipediaen.wikipedia.org
- Introduction to Probability by Joseph K. Blitzstein, Jessica Hwang (z-lib.org).pdfuni.dcdev.ro
- Statlect, the digital textbook | Probability, statistics, matrix algebrastatlect.com
- Stanford University CS231n: Deep Learning for Computer Visioncs231n.stanford.edu
- 275A, Notes 0: Foundations of probability theory | What's newterrytao.wordpress.com
- Syllabuscs230.stanford.edu
- Review of Statistical Learning Theory (CS 281A) at Berkeleydanieltakeshi.github.io
- Bayesian programming - Wikipediaen.wikipedia.org
- Gregory Gundersengregorygundersen.com
- Naive Bayes Classifiers - GeeksforGeeksgeeksforgeeks.org
- 254A, Notes 0: A review of probability theory | What's newterrytao.wordpress.com