Foundations of Machine Learning

This unit introduces core machine learning concepts, data practices, and basic algorithms while grounding students in tools and ethical considerations needed to run small experiments. Students will learn to prepare and work with data, implement simple models, and set up cloud-based tools to support continued study in subsequent units.

Learning Objectives

• Analyze the role of data in machine learning by identifying common data sources, assessing data quality, and determining legal and ethical requirements (including permission and consent) for use in projects
• Apply foundational preprocessing and modeling techniques by preparing example datasets, measuring similarity and combining features, and implementing a basic perceptron and decision tree to evaluate model behavior
• Demonstrate practical use of tools and infrastructure by creating a Google account, using Google Colab to run notebooks, and comparing hardware options (CPU, GPU, TPU) for small-scale experiments
• Evaluate real-world applications and career contexts by explaining use cases such as natural language processing and e-commerce, interpreting learning curves, and proposing a modest ML project that addresses an identified need