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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
+
+
+
+- Summer situation: 
+- Next Academic Year.
+- Git GUI program selection and simplification of git and github tutorial.
+
+---
+## Topics
+and questions
+
+- Jupyter Notebooks
+	- Planned for:
+		- Control Structures
+		- Functions
+
+- Course Overview
+	- Review and discuss potential changes
+	- Error Module
+
+---
+## 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
+
+
+---
+## 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. algorithms
+- AI types (LLM vs. Reconstructive 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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