Practice Problems

DSC 40A at UC San Diego

This site contains practice problems taken from old DSC 40A exams. We’ve organized problems in two ways:

1. By topic. This makes it easy to practice old exam problems by topic.
2. By past exam. This makes it easy to simulate taking an entire exam.

In all cases, you should work on these problems on paper, since your exams will also be on paper. Even though you will see some multiple choice bubbles, you intentionally cannot select them.

Note that some offerings in the past had one Midterm Exam and one Final Exam, while other offerings had two Midterm Exams and a two-part Final Exam. Typically:

• Midterms, Midterm 1s, and Final Part 1s cover the first 5 weeks of the course (loss functions, empirical risk minimization, linear regression, gradient descent), while
• Midterm 2s and Final Part 2s cover the latter 5 weeks of the course (mostly probability).
• Offerings with a single Final Exam were typically cumulative.

Note that in Spring 2024, we’ve covered more linear algebra concepts (e.g. projecting a vector onto the span of other vectors) than were covered in the past, and our exams will reflect this. Note that gradient descent was not in scope for the midterm, but we did cover it, so gradient descent will appear on the final. We did not cover clustering; clustering will not appear on the final.

Furthermore, note that different exams were offered in different formats (remote vs. in-person) with different time limits (50 minutes vs. 80 minutes vs. 180 minutes) and different levels of allowed resources (student-created notes sheets allowed vs. instructor-created reference sheets only vs. no notes). In Spring 2024, the Final Exam will be 180 minutes long, and students are allowed to bring 2 two-sided index cards (4 inches by 6 inches each) of notes that they write by hand (no iPad).

To get a rough sense of the format of the exam, you can see this past exam PDF.

📊 Topics

In scope for the Spring 2024 Final Exam:

• Summary Statistics and the Constant Model
• Simple Linear Regression
• Multiple Linear Regression and the Normal Equations
• Dimensions of Matrix-Vector Products (taken from DSC 140A)
• Gradient Descent and Convexity
• Foundations of Probability
• Conditional Probability
• Combinatorics

• More to come!

📝 Exams

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