AI lingering to-dos

[retabak]

Lessons 0, D-D Algos, and Training AI must stay in that order.
Cluster & Decision Trees could swap positions in the order.
Regression & SLMs might make more sense in a different order (swap positions).

• create "module 0" - intro to AI, AI versus machine learning

Data-Driven Algorithms

• Synthesize questions in last section
• Ensure answers are provided throughout
• Make a student-friendly version of the starter file to hide confusing code

Training AI (spell check)

• Write lesson goals and student-facing goals
• Write all section overviews
• Include the word “training” (and describe) much earlier in the lesson
• Remove wordy descriptions of 3 dimensions. Add Dorai’s pics to the lesson and images.json; get Dorai to improve pics?
• Add teacher note about zero to Normalize Data section

Decision Trees

• Update remaining crappy images that were created in google slides
• Integrate any lingering feedback from FD and ES in git issue

Clustering

• Write it

Regression

• Write objectives
• Write Part 2 once Pyret functions are finalized

SLMS (plagiarism detection)

• Write objectives
• Write section overviews
• Finish “Role of Language” section once Soekia is ready
• Abbreviate the final (optional) Soekia lesson (e.g., remove temperature intro, as temp is now earlier in the lesson)

Overall

• look for opportunities to cross reference lessons
• glossary
• check book / page lengths
• solutions
• check dependencies
• standards?!
• slides?!