INFO 5368-030: Practical Applications in Machine Learning
Cornell Tech
Spring 2023, 2024, 2025
Course Description
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, product recommendation, and image classification using deep learning. 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.
Prerequisites: CS 2800 or equivalent, linear algebra, probability, and experience programming with Python, or permission of the instructor.
Reading: Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.", 2022.
Introduction to PAML
Learning Outcomes
-
Prepare datasets for a ML task, train and evaluate ML models
-
Understand core challenges of dataset creation including handling missing data, bias, among others
-
Visualize features in datasets to be used for ML tasks
-
Apply, analyze, and identify key differences in regression, classification, clustering, and deep learning algorithms
-
Evaluate model quality using appropriate metrics of performance
-
Build front- and back-end ML pipelines for analysis of ML performance and tools for ML practitioners.
Introduction to HRI PAML
Course Schedule
Introduction to HRI Course
Final Projects
Students propose a new ML application that addresses a real-world problem and provides a front-end and back-end solution to users for well-justified user-cases. Students will select an application area (e.g., robotics, healthcare, social media) search for or collect a dataset to address a problem, build an end-to-end ML pipeline, evaluate the algorithms using standard metrics, create visualization tools to analyze ML performance, create a front- and back-end application.
Course projects will be done in groups of up to 4 students and consist of the following tasks:
-
Application of machine learning to a practical problem of your choice. Improvements to machine learning algorithms.
-
Comparison to three or more machine learning methods
-
Evaluate model performance using two or more metrics.
-
Comparison on one or more benchmarks.
-
Analysis of machine learning models.