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Bayesian Conditional Models
Learn how Bayesian conditional models leverage prior knowledge, posterior updates, and predictive distributions to make principled, uncertainty-aware predictions in machine learning.
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On-line to Batch Conversion
Understanding how online learning algorithms can be used to derive hypotheses with small generalization error in a stochastic setting.
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Randomized Weighted Majority Algorithm
Learn how the Randomized Weighted Majority (RWM) Algorithm leverages probabilistic prediction to minimize regret and defend against adversarial strategies in online learning environments.
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Bayesian Decision Theory - Concepts and Recap
A comprehensive guide to Bayesian decision theory, exploring its key components, point estimation, loss functions, and connections to classical probability modeling.
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Reinforcement Learning - An Introductory Guide
Explore the foundations of intelligence, decision-making principles, and their application in reinforcement learning.