MLDL 101 – Machine Learning Foundations

(Weeks 1–2 | Lecture 6 Hrs / Lab 18 Hrs / Ext 0 Hrs | 24 Total Contact Hrs | 1.0 Semester
Credit)
Students will:

• Design supervised machine learning pipelines from raw datasets.
• Build and validate predictive models using cross-validation.
• Engineer features to optimize model performance and accuracy.
• Evaluate model outputs with metrics such as precision, recall, and ROC-AUC.
• Troubleshoot bias-variance tradeoff to improve generalization.
  Prerequisite: Python programming basics, foundational statistics
  Tools: Python, Scikit-learn