Supervised and Unsupervised Learning

6-7 weeks

LearningRepresentation & Reasoning

Overview

This unit dives into the two main paradigms of machine learning: supervised learning (learning from labeled examples) and unsupervised learning (finding patterns in unlabeled data). Students will implement classification and regression models, understand clustering algorithms, and gain practical experience evaluating model performance. They will work with real datasets and confront issues like overfitting, hyperparameter tuning, and feature scaling.

Learning Objectives

1
Understand the supervised learning paradigm and distinguish between regression and classification.
2
Implement and evaluate supervised models: decision trees, k-nearest neighbors, and logistic regression.
3
Understand the unsupervised learning paradigm and apply K-Means clustering.
4
Use hyperparameter tuning (e.g., k in KNN, number of clusters) to optimize model performance.
5
Interpret model results and think critically about fairness, bias, and real-world implications.

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