INFO 5368

Practical Applications in Machine Learning (PAML)

Course Description

Course information provided by the 2025-2026 Catalog.

This course provides hands-on experience developing and deploying foundational machine learning algorithms on real-world datasets for practical applications including predicting housing prices, document retrieval, and product recommendation, and image classification using artificial neural networks. Students will learn about the machine learning pipeline end-to-end including dataset creation, pre- and post-processing, preparation for machine learning, training and evaluating multiple models. Students will focus on real-world challenges at each stage of the ML pipeline while handling bias in models and datasets.

Enrollment Priority Recommended prerequisite: recommended coursework in Python Programming.

Last 4 Terms Offered 2025SP, 2024SP, 2023SP

Learning Outcomes

• Collect a new dataset and prepare it for a ML task, train a model, and evaluate it.
• Apply regression, classification, clustering, and deep learning algorithms to practical applications.
• Analyze and identify key differences in regression, classification, clustering, and deep learning algorithms.
• Understand core challenges of dataset creation including handling missing data, bias, unlabeled data, among others.
• Represent features in datasets to be used for ML tasks.
• Evaluate model quality using appropriate metrics of performance.

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