This is an archival version of Coding the Law's Fall 2022 course site.
Click the green flag to start. Game by Hiro-Protagonist (Colarusso). See original. This game was made in Scratch, an educational programming language. We introduce coding with Scratch in Level 4 if you want to try your hand at making something similar.

Coding the Law
Suffolk Law School: Fall 2022
by @Colarusso

A self-guided LegalTech Adventure for folks with or without prior coding experience.

Your Mission (Continued)
~1 Hour 20 Minutes

Build on our work from class to formalize your play from the last level. Once you've merged the data from Dewey, Cheetham, and Howe, created some potentially useful features, and turned everything into numbers, upload a csv of your data to GitHub with the name dewey-cheetham-howe.csv. See

https://[your username]

As with last week, I am asking you to follow along below. Unless you do the stretch goal, you will not have to turn any coding work this week, aside from the bit above. You will be asked to do something new (run your cleaned data through some code), but stretch goal aside, there's nothing to turn in. Also, like last week, we will discuss everything in person, and even if you don't do the stretch goal, you will be asked later to build on this work to turn something in, just not this week. That being said, just follow along in your own notebook and do your best.

What the Heck is Word2Vec? Neural Nets for Lawyers
11-33 min. Protip: You can watch YouTube videos at more than 1X speed.

FWIW, preparing instructional material is an exercise in compression, and it's not lossless. The hope is that you now have a very high-level sense of how things work. For example, I glossed over how the activation function behaves with regard to the word2vec "hidden"/projection layer. Spoiler: it's not a sigmoid! Actually, there is no activation function. We just pass on the weights. That being said, I didn't explain weights very deeply. So again, as it says in the title—oversimplification. ;)

Same Stats, Different Graphs
Source: Autodesk.

Optional Media. If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following.

~ 1 Hour and 10 Minutes

Training Your Algos: Linear Regressions
5-14 Minutes.

Note: none of your missions will ask you to perform a linear regression. So don't worry too much about the details discussed above. Given how much we've talked about regressions, I figured we should build at least one together as a class.

We are working with the notebook file training.ipynb (pre-loaded for those of you using Pythonanywhere).

Optional: If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following: Guess the Correlation (a video game where you guess the R-squared).

Training Your Algos: Binary Classifiers
9-26 Minutes.

Again, we are working with the notebook named training.ipynb (pre-loaded for those of you using Pythonanywhere).

I added a section to the notebook that isn't covered in the video above (Making Predictions). It's a bit of context that you may find helpful later. Wink, wink, nudge, nudge. ;)

Optional: If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following.

Your Mission (Continued)
~1 Hour

Take the data you prepared above (i.e., https://[your username] and use it to train the classifiers found in training.ipynb under the Classifiers Section. Remember I've only loaded libraries for the Python 3.5 kernel. So if you make a new notebook, be sure it's using 3.5.

Stretch Goal: Add a new classification algo from scikit-learn to your notebook. Take a screen shot of the evaluation screen for this algo, and upload it to GitHub with the name new_algo. E.g.,

https://[your username]

Note you should follow the pattern of the algos shown above. That is, you can just copy one of the example cells, swap out the first two lines and edit variable names accordingly. You might find some inspiration here.

Your Final Project

Enrolled students will be presenting on their final project in one week. Take whatever time you have left to work on your project, even if it's just planning or skills acquisition (e.g., working through the optional docassemble training from level 3). See The Final Project Rubric.

Self-Reflection and Logging Your Work
~20 min

As we do at the end of every level, we ask that you take a few minutes to reflect on how things are going. I've also included a set of reading questions to queue things up for our synchronous discussion. Your answers will be shared with me and it will let me know that I can look for any project work you may have posted. That being said, you've almost completed Level 7. Tell me how it's going by completing the form linked below.

Synchronous Meet Up, AKA our Class Time
~1.8 hours | October 17, 2022 @ 4pm Eastern

If you're an enrolled student, we'll be meeting in Sargent Hall Room 325 on Monday October 17th at 4pm. Our remote backup is to meet via Zoom at this link. You should have received the password from me earlier. If you don't have the password, and you are a registered student, DM me on Teams, and I can give you the password. If you're not an enrolled student, I'm afraid you can't join us.

We will use this time to: (1) troubleshoot any issues folks might have had working through the your mission; and (2) discuss the readings.

Time estimates are just that—estimates. The assumptions used to calculate reading time are as follows: 48 pages is assumed to take roughly an hour to read. When working with non paginated texts, it is assumed that a page is roughly equal to 250 words. Videos assume both 3X and 1X viewing. Estimates for coding are based on past experience. Each level should include about 6 hours and 40 min of work.