Midterm Exam
Logistics
Date/Time:
- Midterm: Tuesday 4/15 in class
- Retest: Thursday 5/1 in class
Due to students admitting to using ChatGPT on the midterm when notes could be used on their computers, the retest is not open-note. You may bring one 8.5x11 cheatsheet. Standalone calculators will be provided for students who would like to use them.
Practice Exam
There is a practice exam available here and a solution here. Some problems are pulled directly from worksheets or adapted from them, but should give you a sense of the type of questions that will be asked. Expect this practice exam to take slightly longer than the real exam.1
Retest
There is a midterm in this class and an optional retest. All questions are graded credit/no credit by subpart (subparts are clearly marked). There is an opportunity to take a retest to attempt questions that you did not get full credit on the first time. For instance, imagine that you get the following on the initial exam:
| Question | Points Earned |
|---|---|
| Regular Expressions | 0.6 |
| Naive Bayes | 1 |
| Part-of-Speech Tagging | 1 |
| Word and Document Representations | 1 |
| Logistic Regression & Neural Networks | 0.5 |
| Transformers & BERT | 1 |
| Text Generation | 0 |
| Evaluating Models | 0.5 |
Your total score is 5.6, so if you’ve completed all homework assignments with 3/3 points, you might decide not to show up for the retest, which is totally fine! You would have earned 26.6 points in the methods category, which is enough for the A-tier in that area.
If you haven’t completed all of the homework assignments yet, you might want to try to improve your exam score. You’ve already earned the maximum possible points for Naive Bayes, Part-of-Speech Tagging, Word Representations, and Transformers & BERT, so you should not attempt those problems on the retest. Let’s say you attempt Regular Expressions, Logistic Regression & Neural Networks, Text Generation, and Evaluating Models, and get the following scores:
| Question | Points Earned |
|---|---|
| Regular Expressions | 0.2 |
| Logistic Regression & Neural Networks | 0 |
| Text Generation | 1 |
| Evaluating Models | 1 |
Your final score is the max of the points earned for each problem across the two exams, which would be as follows:
| Question | Points Earned |
|---|---|
| Regular Expressions | 0.6 |
| Naive Bayes | 1 |
| Part-of-Speech Tagging | 1 |
| Word and Document Representations | 1 |
| Logistic Regression & Neural Networks | 0.5 |
| Transformers & BERT | 1 |
| Text Generation | 1 |
| Evaluating Models | 1 |
Your final exam score would be 7.1.
Footnotes
Because some problems are from worksheets, I’d recommend against using worksheet solutions while completing these problems if you’re trying to get realistic practice for the exam.↩︎