Explores bit error rate and receiver sensitivity in optical communication systems, covering BER, receiver sensitivity, probability density functions, and error probability calculations.

Convergence Analysis: Stochastic Gradient Algorithms

Explores the convergence analysis of stochastic gradient algorithms under various operational modes and step-size sequences.

Statistical Models: Sampling and Hypothesis Testing

Explores statistical models, sampling distributions, and hypothesis testing using real-world examples.

Loss Functions and Optimization

Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.

Quadratic Penalty Methods: Sound Problems

Explores Quadratic Penalty Methods for optimization with enforced constraints using penalty functions.

Statistical Theory: Decision Theory Framework

Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.

PAC learning framework

Covers the PAC learning framework, including domain, feature set, classifier, and empirical risk.

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