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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.
+	- No funding from grad school for the summer.
+	- Summer job lined up -> Gone for ~5 weeks.
+	Plan to work on creating content for each module.
+
+- GTA Offer Application for next academic year
 
 ---
 ## Topics
 and questions
-
-- Jupyter Notebooks
-	- Planned for:
-		- Control Structures
-		- Functions
-
-- Course Overview
-	- Review and discuss potential changes
-	- Error Module
+- Git GUI: Smart Git
+	- Available on: Windows, Mac and Linux
+	- Will write a crash-course
+- Started working on module 3
+	- Add taylor series as an example at the end of differentiatlion tutorial
 
 ---
 ## Discussion 
+- After finals week, write GTA application on the 14th/15th then Ciprian can revise.
+- Github desktop - Play around with it. then compare with smart Git.If smilar then go for github desktop
 
-- 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
+- Adding -> theory at the start and more dense, talke about fundamentals (what is a derivative?)
 
+- Format of tutorials:
+	- Theory
+	- Engineering -> Have students think about the problem and come up with 
+	- Coding -> 'solution' 
+		- exaercises
 
 ---
 ## 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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