The course is conducted in English。

This course, Computer Programming in Python, introduces students to the fundamentals of Python programming. We begin with core language concepts, then explore how to visualize data through plotting. The course further demonstrates how Python can be applied to artificial neural networks and concludes with practical examples in physics and materials science, equipping students with programming skills relevant to modern scientific research. Syllabus (topic to cover in this course): 1. Introduction to Python programming 2. Setting up the development environment (VS Code and JupyterLab) 3. Basic Python programming (variables, data types, strings, etc.) 4. Intermediate and advanced Python (classes, algorithms, etc.) 5. Data visualization with Matplotlib 6. Introduction to machine learning and neural networks 7. Building neural networks from scratch 8. Advanced neural networks with TensorFlow or PyTorch 9. Python for electronic structure calculations Assessments: * Mid-semester examination (50%): 3 hours * Final examination (50%): 3 hours

1) Understand and apply Python programming concepts such as variables, control flow, functions, and data structures. 2) Create visualizations using Matplotlib libraries to analyze scientific data. 3) Develop and train basic machine learning for neural networks using Python libraries (e.g., TensorFlow or PyTorch) 4) Apply Python to solve problems in physics and materials science through simple simulation.

Need to bring your own laptop to class

2 hours

Textbook or Designated Reading: - Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming, Eric Matthes, 2019 - Fluent Python: Clear, Concise, and Effective Programming, Luciano Ramalho, 2015 - Python Data Science Handbook: Essential Tools for Working with Data, Jake Vanderplas, 2017 - Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurelien Geron, 2017 - Introduction to Solid State Physics, Charles Kittel, 2004 Note: Additional journal articles, reviews, and materials will be given out in classes. Other References: * GitHub: - https://github.com/nguyen-group/Neural_Networks_with_Python - https://github.com/Asabeneh/30-Days-Of-Python - https://github.com/CodeWithHarry/The-Ultimate-Python-Course