Exams

There will be two exams in CSE/STAT 416 this quarter:

• Midterm: A take-home midterm

• Final: An in-person final (paper) during our final exam slot on Monday, 6/3/2024 at 6:30 PM - 8:20 PM. Location is CSE2 G20 (same as lecture hall)

Final¶

The final exam will cover the various topics of machine learning that we have learned this quarter.

The final exam is on Monday, 6/3/2024 at 6:30 pm - 8:20 pm. You will have the full exam period (1 hour and 50 minutes) to complete the exam. You are allowed a single A4 notesheet (both sides) with you during the exam. You should have no electronic devices or any of your own scratch paper on your desk; you should just have your student ID, writing utensils, your notesheet, a water bottle and the exam. Any violation of these rules will result in a 0 on the entire test.

If you require extra time and receive DRS accommodations, you must get in touch with the instructor early on to schedule a separate final exam session ahead of time.

Resources¶

Here are some review materials that have been put together by past and current course staff. Like with training a good ML model, you will want to use good training practices to make sure you properly assessing your understanding of the material. We recommend that you save the practice exam until later in your studying so that you can use it as a un-biased estimate of your test accuracy. When taking the practice exam, try to take it like you would the real exam (i.e. time yourself, try to do the whole thing without breaks our looking at your notes).

Study resources

Note that these aren’t meant to replace your own study materials made from your notes and learning reflections, but might be helpful references as well!

• Exam Topics

• Review Notes

• Review Questions

• Class calendar

  • Past slides

  • Checkpoints

  • Section handouts

Practice Exams

• Practice Exam 1 (Solutions)

• Practice Exam 2 (Solutions)

Study Strategies¶

• Look over slides and do practice problems (from lectures, sections, checkpoints, assignments).

• Make sure you understand the correct responses in concept questions from assignments. You can view these on Gradescope. Post on the discussion board if any are confusing.

• You should be able to explain for each technique:

  • What types of problems it can be used for

  • How it works (key ideas)

  • Challenges (overfitting, having to choose hyperparameters, etc)

• Also, you should be able to explain general ML concepts such as overfitting, bias-variance tradeoff, precision/recall, etc. You can practice by pretending you’re being asked about these concepts at an interview.

• Use your learning reflections to help you review. Try building up your cheat sheet before taking and practice exams. Save the practice exams til the end and use those to assess your perforamnce.