Quantum Decision-Making: Neural Network Privacy

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Related lectures (29)

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Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Conditional Density and Expectation

Explores conditional density, expectations, and independence of random variables with practical examples.

Privacy Technologies and Data Protection

Explores privacy technologies, data protection, surveillance risks, and Privacy Enhancing Technologies for social and institutional privacy.

Topological Data Science

Delves into Topological Data Analysis, emphasizing the mathematical foundations of neural networks and exploring the manifold hypothesis and persistent homology.

Probability and Statistics

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Désanonymisation: Risks and Techniques

Explores risks and techniques in de-anonymizing data, including flaws in methods and real-world examples of failed attempts.