1.Introduction to computer programming: fundamental concepts, syntax, and structure of programming languages(1)
2.Introduction to computer programming: fundamental concepts, syntax, and structure of programming languages(2)
3.Basic data types and control structures: variables, operators, loops, and decision-making(1)
4.Basic data types and control structures: variables, operators, loops, and decision-making(2)
5.Object-oriented programming: classes, objects, inheritance, and polymorphism(1)
6.Object-oriented programming: classes, objects, inheritance, and polymorphism(2)
7.Algorithm design and problem-solving: principles of good design and techniques for solving common programming challenges(1)
8.Algorithm design and problem-solving: principles of good design and techniques for solving common programming challenges(2)
9.Composition in computer programming: techniques for combining and reusing code, such as functions, modules, and libraries(1)
10.Composition in computer programming: techniques for combining and reusing code, such as functions, modules, and libraries(2)
11.Web development: introduction to HTML, CSS, and JavaScript for creating dynamic web pages(1)
12.Web development: introduction to HTML, CSS, and JavaScript for creating dynamic web pages(2)
13.Database programming: design and implementation of databases and SQL(1)
14.Database programming: design and implementation of databases and SQL(2)
15.Testing and debugging: methods for identifying and resolving errors in code(1)
16.Testing and debugging: methods for identifying and resolving errors in code(2)
17.Project development: an opportunity to apply the concepts and techniques learned in the course to a final project.(1)
18.Project development: an opportunity to apply the concepts and techniques learned in the course to a final project.(2)

https://sites.google.com/itd.tnnua.edu.tw/mst-musai/

1. Algorithm and Machine Learning
2. Greedy Algorithm
3. Divide and Conquer
4. Dynamic Programming
5. Branch and Bound
6. Simulated Annealing
7. Genetic Algorithm
8. Model and Learning
9. Regression and Classification
10. Supervised Learning
11. Unsupervised Learning
12. Decision Trees and Search
13. Clustering: KNN and K-Means
14. Support Vector Machine (SVM)
15. Regression Analysis
16. Reinforcement Learning

Week1 Course introduction and classroom rules
Week2 Metaverse fundamental I – technology
Week3 Metaverse fundamental II - ecology
Week4 Business model fundamental
Week5 Business model case study
Week6 Metaverse market – currency I
Week7 Metaverse market – currency II
Week8 Metaverse market – NFT I
Week9 Metaverse market - NFT II
Week10 Mid-term presentation
Week11 Mid-term presentation
Week12 Business model comparison and contrast
Week13 Metaverse market evolution/forecast
Week14 Meta-only business
Week15 Meta-citizen – dweller vs. investor
Week16 Review
Week17 Project Presentation
Week18 Project Presentation