## Updates
- Schedule, couple days behind schedule
- Focused on core content (tutorials)
- Working on Jupyter Notebooks. Module 1 & 2 -> notebooks
- Have a compiled pdf / textbook.
- Workflow: still writing in .md -> convert to .ipynb & .tex

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## Topics
and questions

- Jupyter Notebooks
	- Planned for:
		- Control Structures
		- Functions

- Course Overview
	- Review and discuss potential changes
	- Error Module

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## Discussion 

- AI programming tutorial 
	- Types of AI
		- Language
		- Vision
		- Generative
		- Reinforming 
- AI vs Algorithms
	- Rubics Cube example of algorithm
- VCS tutorial -> Github vs. git

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## Actions

To do:  

- Each Tutorial should have two problems (one to work in classroom, one to leave as homework)
- Intro to algorithm at the beginning of Module 2 (computational algorithm vs. real-world algorithms)
- Move AI at the end of Module 2
- AI vs. alorithms
- AI types (LLM vs. Resconstructive AI vs. Generative AI, Reinforcement AI, Vision AI etc)
- Then we go into AI applications: AI for code debugging vs AI for code generation based on flowcharts
- pick a GIT GUI program for next time and see how much we can simplify the discussion on Git and Github
- Module 3 - bare minimum numerical methods: 1. Equation solvers/Root finding: Newton, Secant Method. 2. Systems of Equations: Gauss Method, LU Decomposition. 3. Integration: Trapezoid Method, Simpson Method. 4. Differentiation/ODEs: Explicit Euler, RK methods, Implicit Euler.