INFO 5368 - Practical Applications in Machine Learning (PAML) (3 Credits)  -  A. Taylor

Note: Most of the lectures involve in-class programming; visiting students will need to reach out to the professor ahead of time to receive access to the notebook used in the lecture. Upon registration, please email admissions for the professor's contact information.

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 Information: Recommended prerequisite: recommended coursework in Python Programming.  
Last Four Terms Offered: Spring 2025, Spring 2024, Spring 2023  
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.