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Resources đź“š

Resources will be added here as the quarter progresses.

Table of contents

  1. Videos from Previous Quarters
  2. Probability
    1. Readings
    2. Roadmap
    3. Visualizations
  3. Past Exams and Videos
  4. Other Resources

Videos from Previous Quarters

In the table below, you can find lecture videos created by Janine Tiefenbruck, who created this course and taught it many times. The lecture videos linked below will generally be pretty similar in content coverage to our lectures, but there will be occasional differences. You are responsible for everything covered in our lectures, even if something doesn’t appear in the videos below. When in doubt, refer to the main lecture slides posted and ask on Campuswire.

#Lecture TitleLinks
1Introduction, Learning From DataVideo 1
2Mean Absolute ErrorVideo 2
first 10:45 of Video 3
3Mean Squared Error and Empirical Risk Minimization10:45 onwards of Video 3
Video 4
4Spread, Other Loss Functions, and Gradient DescentVideo 8
Video 5
first 5:00 of Video 6
5Gradient Descent and Convexity5:00 onwards of Video 6
Video 7
6Simple Linear RegressionVideo 9
Video 10
first 4:30 of Video 11
7More Simple Linear Regression4:30 onwards of Video 11
first 17:00 of Video 13
(correlation is not covered in past lectures)
8Regression and Linear Algebra17:00 onwards of Video 13
Video 14
9Multiple Linear Regression and Feature EngineeringVideo 16
Video 15
10Feature Engineering, ClusteringVideo 15
Video 17
11Clustering, Introduction to ProbabilityVideo 18
first 10:00 of Video 19
12Foundations of Probability10:00 onwards of Video 19
Video 20
13CombinatoricsVideo 21
Video 22
14More Combinatorics, Conditional ProbabilityVideo 23
Video 24
15IndependenceVideo 25
16Naive BayesVideo 26
17More Naive BayesVideo 27


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.


  • 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 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 (taught by Suraj a few years ago) 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.


During the Spring 2021 offering of the course, Janine Tiefenbruck wrote a “Probability Roadmap” that aims to guide students through the process of solving probability problems.

  • 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 concise and contains only the strategies themselves.


Past Exams and Videos

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:

  • In many earlier offerings of the course, there were two Midterms – Midterm 1 covered empirical risk minimization, and Midterm 2 covered probability. In addition, often times Part 1 of the Final covered content similar to that of Midterm 1 (empirical risk minimization), and Part 2 of the Final covered content similar to that of Midterm 2 (probability).
  • Topic coverage and ordering has changed over time, so the content in our Midterm won’t necessary match the content of earlier Midterms/Midterm 1.

We’ve created video walkthroughs of a few problems from past exams. We will try to make more as the quarter progresses. You can find these videos linked in the table below by exam, or click this link to see a playlist of them all.

QuarterInstructor(s)Midterm/Midterm 1Midterm 2Final
Fall 2021Suraj RampureBlank, 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!