Machine Learning
Machine learning theory and applications
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Deploying ML models: pipelines, monitoring, scalability, and MLOps.
2025-2026 Spring
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MDPs, Q-learning, policy gradients, and deep reinforcement learning.
2025-2026 Spring
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Ensemble methods, kernel machines, graphical models, and reinforcement learning.
2025-2026 Spring
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Supervised and unsupervised learning, regression, classification, and clustering.
2025-2026 Fall