INFO 5368

Practical Applications in Machine Learning (PAML)

Course Description

Course information provided by the 2022-2023 Catalog.

This course provides hands-on experience developing and deploying foundational machine learning algorithms on real-world datasets for practical applications (e.g., healthcare, computer vision). Students will learn about the machine learning pipeline end-to-end including dataset creation, pre- and post-processing, annotation, annotation validation, preparation for machine learning, training and testing a model, and evaluation. Students will focus on real-world challenges at each stage of the ML pipeline while handling bias in models and datasets. Lastly, students will analyze the strengths and weaknesses of regression, classification, clustering, and deep learning algorithms.

Prerequisites/Corequisites Prerequisite: CS 2800 or equivalent, linear algebra, probability, differential equations and experience programming with Python, or permission of the instructor.

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

When Offered Spring.

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