MACHINE LEARNING

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Identify different aspects of machine learning, including supervised learning, unsupervised learning, and reinforcement learning Audience: Graduate 2. Implement and analyze a variety of supervised models for classification and regression, including decision trees, instance-based models, naive Bayes, support vector machines, a variety of neural networks, linear and logistic regression, and others Audience: Graduate 3. Implement and analyze neural network models, starting with the perceptron, and continuing to multilayer perceptrons, convolutional neural networks, recurrent neural networks, along with deep generative models Audience: Graduate 4. Identify various types of regularization techniques and their properties Audience: Graduate 5. Implement optimization techniques used in modern machine learning, including gradient descent and stochastic gradient descent Audience: Graduate 6. Apply various concepts and metrics involved in evaluating models: accuracy, F measures, ROC, and precision/recall curves, and implement cross-validation Audience: Graduate 7. Analyze unsupervised learning techniques for clustering, dimensionality reduction, and latent models Audience: Graduate 8. Identify classical and modern techniques to improve models or deal with dearth of data: ensemble methods, semi-supervised learning, weak supervision Audience: Graduate Learning Outcomes: 1. Identify different aspects of machine learning, including supervised learning, unsupervised learning, and reinforcement learning Audience: Graduate 2. Implement and analyze a variety of supervised models for classification and regression, including decision trees, instance-based models, naive Bayes, support vector machines, a variety of neural networks, linear and logistic regression, and others Audience: Graduate 3. Implement and analyze neural network models, starting with the perceptron, and continuing to multilayer perceptrons, convolutional neural networks, recurrent neural networks, along with deep generative models Audience: Graduate 4. Identify various types of regularization techniques and their properties Audience: Graduate 5. Implement optimization techniques used in modern machine learning, including gradient descent and stochastic gradient descent Audience: Graduate 6. Apply various concepts and metrics involved in evaluating models: accuracy, F measures, ROC, and precision/recall curves, and implement cross-validation Audience: Graduate 7. Analyze unsupervised learning techniques for clustering, dimensionality reduction, and latent models Audience: Graduate 8. Identify classical and modern techniques to improve models or deal with dearth of data: ensemble methods, semi-supervised learning, weak supervision Audience: Graduate