95-828 Machine Learning for Problem Solving
12 units, Carnegie Mellon University
Machine Learning (ML) is centered around automated methods that improve their own performance through learning patterns in data, and then using the uncovered patterns to predict the future and make decisions. ML is heavily used in a wide variety of domains such as business, finance, healthcare, security, etc. for problems including display advertising, fraud detection, disease diagnosis and treatment, face/speech recognition, automated navigation, to name a few. "If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." -- Albert Einstein "A problem well put is half solved." -- John Dewey This course provides graduate students with a comprehensive and practical grounding in modern machine learning. The goal is to build a strong intuitive understanding of machine learning concepts, models, and workflows; anchored in real-world applications rather than abstract theory. "All models are wrong, but some models are useful." -- George Box Because there is no universally best model, this course surveys a wide range of machine learning methods and learning paradigms, each offering distinct tradeoffs in speed, accuracy, and interpretability. The curriculum progresses from classical supervised learning—covering linear and logistic regression, decision trees, ensemble methods, kernel, and nonparametric models—to modern deep learning architectures, including neural networks, CNNs, RNNs, LSTMs, transformers, and diffusion models. The course also explores unsupervised learning techniques such as clustering, anomaly detection, and dimensionality reduction, and concludes with machine learning on specialized data modalities, including tabular, graph, temporal, text, and image data. See linked URL for full course description.
Prerequisites: none listed
Offered: Spring 2027, Spring 2026
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