Course Learning Outcomes

By the end of this course, students should be able to:

• Describe a practical ML workflow from problem framing through review.
• Explain training data, labels, features, loss, and evaluation in human language.
• Recognize overfitting, data leakage, and weak test sets.
• Build a simple evaluation plan for a classifier or AI-assisted workflow.
• Decide when a model result is not ready for real users.