• To learn the terminology associated with machine learning (e.g., train-test splits, cross-validation)
• To learn how to execute an end-to-end machine learning pipeline using the scikit-learn library
• To learn various techniques for inspecting how a machine learning model makes its decisions
IMPORTANT: This module focuses on “traditional” machine learning models like logistic regressors and random forest classifiers. It does not cover state-of-the-art deep learning like large language models (LLMs) or foundational models. However, the last lecture will show how the concepts covered in this module are prerequisite for these advanced model architectures.
Lecture 1: Machine Learning Terminology and Workflow
• Crash Course on Machine Learning
Lecture 2: Our First Models
• Classification with an Image Dataset: Skin Cancer
• Regression with a Time-Series Dataset: Parkinsonian Gait
