Course Title:
Python and AI
UC Subject Category: Mathematics – Computer Science (C)
Delivery Mode: Online & Classroom-Based
Grade Levels: 10–12
Credits: 5 UC-Approved High School Credits
Prerequisites: Algebra I (Algebra II recommended)
Course Description:
Python and AI introduces students to the foundations of programming with Python and its powerful applications in artificial intelligence. This course blends computational thinking, algorithmic design, and practical coding skills with introductory machine learning concepts. Students will write and debug Python code, work with data structures, and build small AI models using popular libraries such as NumPy, pandas, and Scikit-learn.
As students develop coding fluency, they will apply their skills to real-world challenges involving data analysis, natural language processing, computer vision, and predictive modeling. Ethical issues surrounding AI—including algorithmic bias and data privacy—are integrated throughout the course to encourage responsible and thoughtful design.
By the end of the course, students will have completed a capstone project that demonstrates their understanding of both Python programming and AI principles. The course prepares students for further study in computer science, data science, and related STEM fields.
Syllabus Overview: Python and AI
| Unit | Topics Covered | Key Activities & Assessments |
|---|---|---|
| Unit 1: Introduction to Python Programming | Variables, data types, loops, conditionals, functions | Coding labs, debugging challenge, basic text-based game |
| Unit 2: Working with Data | Lists, dictionaries, file I/O, CSV parsing, data visualization | Build a grade calculator; plot data with matplotlib |
| Unit 3: Python Libraries for AI | NumPy arrays, pandas dataframes, data preprocessing | Data cleaning and analysis with real-world datasets |
| Unit 4: Introduction to Machine Learning | Supervised learning, classification, training/testing datasets | Create a model to classify movie reviews or predict grades |
| Unit 5: Natural Language Processing (NLP) | Text tokenization, sentiment analysis, word frequency | NLP mini-project with user-generated input and results dashboard |
| Unit 6: Computer Vision | Image arrays, filters, object recognition, OpenCV basics | Build a basic image classifier or photo filter engine |
| Unit 7: Ethics in AI and Technology | AI bias, fairness, deepfakes, data privacy | Write a research-based essay or podcast episode on an ethical issue |
| Unit 8: Capstone Project | Student-directed project combining Python and AI techniques | Final project with report, code submission, and presentation |
Tools & Platforms
- Programming: Google Colab, Replit, or Jupyter Notebook
- Libraries: NumPy, pandas, matplotlib, Scikit-learn, NLTK, OpenCV
- Learning Management: Canvas LMS
- Optional tools: Kaggle datasets, Teachable Machine, DALL·E for visualization
Grading Breakdown
- Weekly coding exercises and labs – 30%
- Quizzes and technical assessments – 15%
- Ethics discussion assignments – 10%
- Unit projects – 25%
- Final Capstone Project – 20%
Student Learning Outcomes
By the end of the course, students will:
- Write Python code to solve computational problems
- Use Python libraries to manage and analyze structured data
- Build and evaluate basic AI and machine learning models
- Understand key AI applications in NLP and computer vision
- Analyze and communicate the ethical implications of AI systems
- Complete and present a self-directed coding and AI project