Week 1: Introduction to Machine Learning + Regression

Week 1 will introduce students to core machine learning ideas and guide them through their first modeling task using linear regression. The emphasis will be on learning the language and structure of ML: supervised vs. unsupervised learning, training vs. test data, model evaluation, and pitfalls like overfitting.

Focus

• What is Machine Learning: what problems does it solve?
• Supervised vs. Unsupervised learning
• Regression with scikit-learn
• Evaluation metrics for regression
• Conceptual introduction to overfitting and underfitting

Hands-On Activities

• Use scikit-learn to train a linear regression model
• Explore and clean a tabular dataset
• Visualize regression predictions and residuals
• Split data into training and test sets
• Plot training vs. testing performance to identify overfitting

Learning Outcomes

By the end of this week, students will be able to:

• Explain the basic idea of machine learning and how it relates to AI.
• Identify regression vs. classification problems
• Use scikit-learn to train a linear regression model
• Evaluate regression using MSE, MAE, and R²
• Describe overfitting and underfitting using visuals and examples

Resources

• scikit-learn Regression Docs
• Visual examples of overfitting

Instructor Notes

• Encourage students to focus more on intuition than math
• Emphasize that regression is a stepping stone to more complex models
• Keep datasets simple and visual where possible