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Table of contents

  1. Lecture Videos
  2. Course Notes
  3. Probability
    1. Readings and Sources of Practice Problems
    2. Probability Roadmap
    3. Visualizations
  4. Past Exams
  5. Other Resources

Lecture Videos

These are the lecture videos, which you should watch asynchronously by the date listed. We’ll use our scheduled class time for further engagement with the material through groupwork, extra practice, and office hours for homework help.

VideoWatch byTopics
Video 1Sunday, January 9learning from data, mean absolute error
Video 2Sunday, January 9minimizing mean absolute error
Video 3Sunday, January 9mean squared error
Video 4Sunday, January 9empirical risk minimization, general framework, 0-1 loss
Video 5Sunday, January 16UCSD loss
Video 6Sunday, January 16gradient descent
Video 7Sunday, January 16gradient descent demo, convexity
Video 8Sunday, January 16spread
Video 9Sunday, January 23linear prediction rule
Video 10Sunday, January 23least squares solutions
Video 11Sunday, January 23regression interpretation
Video 12Sunday, January 23nonlinear trends
Video 13Sunday, January 30linear algebra for regression
Video 14Sunday, January 30gradient, normal equations
Video 15Sunday, January 30polynomial regression, nonlinear trends
Video 16Sunday, January 30multiple regression
Video 17Sunday, February 6k-means clustering
Video 18Sunday, February 6k-means clustering, cost function, practical considerations
Video 19Sunday, February 13probability, basic rules
Video 20Sunday, February 13conditional probability
Video 21Sunday, February 13probability, random sampling, sequences
Video 22Sunday, February 20combinatorics, sequences, sets, permutations, combinations
Video 23Sunday, February 20counting and probability practice
Video 24Sunday, February 20law of total probability, Bayes’ Theorem
Video 25Sunday, February 27independence, conditional independence
Video 26Sunday, February 27naive Bayes
Video 27Sunday, February 27text classification, spam filter, naive Bayes

Course Notes

The notes for this class were written by me and Justin Eldridge. These notes cover the material from the first half of the course and align very closely with the material you’ll see in the lecture videos.


Unlike the first half of the course, where we had course notes written specifically for this class, we don’t have DSC 40A-specific notes for the second half of the class, because there are many high-quality resources available online that cover the same material. Below, you’ll find links to some of these resources.

Readings and Sources of Practice Problems

  • Open Intro Statistics: Sections 2.1, 2.3, and 2.4 cover the probability we are learning in this course at a good level for undergraduates. This is a good substitute for a textbook, similar to the course notes that we had for the first part of the course. It goes through the definitions, terminology, probability rules, and how to use them. It’s succinct and highlights the most important things.

  • Probability for Data Science: Chapters 1 and 2 of this book have a lot of good examples demonstrating some standard problem-solving techniques. This book should be primarily useful for more problems to practice and learn from. This book is written at a good level for students in this class. It is used at UC Berkeley in their Probability for Data Science course. Our course only really covers material from the first two chapters, but if you want to extend your learning of probability as it applies to data science, this is a good book to help you do that.

  • Theory Meets Data: Chapters 1 and 2 of this book cover similar content to Chapters 1 and 2 of the Probability for Data Science book, but with different prose and examples. It is used at UC Berkeley for a more introductory Probability for Data Science course.

  • Grinstead and Snell’s Introduction to Probability: Chapters 1, 3, and 4.1 of this book cover the material from our class. This book is a lot longer and more detailed than the others, and it uses more formal mathematical notation. It should give you a very thorough understanding of probability and combinatorics, but it is a lot more detailed, so the more abbreviated resources above will likely be more useful. With that said, this book is written at a good level for undergraduates and is used in other undergraduate probability classes at UCSD, such as CSE 103.

  • Introduction to Mathematical Thinking: This course covers topics in discrete math, some of which are relevant to us (in particular, set theory and counting). In addition to the lecture videos linked on the homepage, you may want to look at the notes section.

  • Khan Academy: Counting, Permutations, and Combinations: Khan Academy has a good unit called Counting, Permutations, and Combinations that should be pretty helpful for the combinatorics we are learning in this class. A useful aspect of it is the practice questions that combine permutations and combinations. Most students find that the hardest part of these counting problems is knowing when to use permutations and when to use combinations. These practice questions have them mixed together, so you really get practice learning which is the right technique to apply to which situation.

Probability Roadmap

I wrote a “Probability Roadmap” that aims to guide students through the process of solving probability problems. I hope you’ll find it useful! It comes in three versions:

  • Examples: This document consists of strategies followed by example problems that employ those strategies. If you’re looking to gain additional practice, start here.
  • Solutions: This document contains solutions and explanations for all of the example problems in the first document. After you’ve attempted the problems on your own, read through this full document. Even if you’ve solved all the questions, you’re likely to learn how to do some problems in new ways.
  • Summary: This document is a concise summary and contains only the strategies themselves.


Past Exams

Below, you’ll find some exams (and in some cases, their solutions) from previous offerings of the course. You must be logged into your Google account to access these.

Some things to keep in mind:

  • Certain offerings of the course had one midterm and others had two. Usually, Midterm 1 covered empirical risk minimization, and Midterm 2 covered probability.
  • Topic coverage and ordering has changed over time, so the content in our exams won’t necessarily exactly match the content of these past exams.
  • Some of these exams were given as closed-book exams and others allowed the use of resources.
QuarterInstructor(s)Midterm/Midterm 1Midterm 2Final
Fall 2021Suraj RampureBlank, Solutions–Blank, Solutions
Spring 2021Janine TiefenbruckBlank, Solutions, Videos 🎬–Part 1: Blank, Solutions
Winter 2021Gal MishneBlank, SolutionsBlank, SolutionsPart 1: Solutions
Part 2: Solutions
Fall 2020Janine Tiefenbruck, Yian MaBlank, Solutions–Part 1: Blank, Solutions
Spring 2020Janine TiefenbruckBlank, Videos 🎬–Part 1: Blank
Winter 2020Justin EldridgeSolutionsSolutionsSolutions

Other Resources

If you find another helpful resource, let us know and we can link it here!