95-897 Machine Learning Technologies
6 units, Carnegie Mellon University
This course offers a foundational introduction to the principles, methodologies, and applications of modern machine learning (ML); from foundational principles to cutting-edge methods across diverse data modalities. It is organized into three broad parts. Part I focuses on the foundations of supervised learning, emphasizing classical models such as linear and logistic regression, decision trees, and ensemble methods. The goal is to equip students with the understanding of the ML workflow, model evaluation, and generalization principles. Core concepts such as bias-variance tradeoff, interpretability, uncertainty, robustness, and fairness are introduced early, preparing students to think critically about model performance and reliability. This part also introduces the contemporary learning paradigms including self-supervised, semi-supervised, active, meta, continual, reinforcement, and federated learning to showcase the diverse settings under which ML is developed. Building on this foundation, Part II introduces modern models including neural networks and advanced architectures such as convolutional, recurrent, and attention-based models. Finally, Part III focuses on ML techniques for specialized data modalities—tabular, graph, temporal, text, and image data—and their practical applications such as forecasting, recommendation, fraud detection. By the end of the course, students will have a comprehensive understanding of both the theoretical underpinnings and the practical considerations involved in designing and applying machine learning techniques.
Prerequisites: none listed
Offered: Spring 2027, Spring 2026
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