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## 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
- reinforcement learning
- Hands on exercise using AI flowshort > code or debug code.
- AI vs Algorithms
- Introduce **rubric cube** as solving an algorithm
- 2 exercises per week
VCS
- Git -> Git vs github
- instead of git use GUI
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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.
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