Lectures related to Lévy distribution

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Explores robustness in visual intelligence, addressing distribution shifts, failure examples, and strategies for improvement.

Radiative Exchange: Specular View Factors

Covers specular view factors, radiative exchange, energy transfer, and numerical integration methods in thermal radiation.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Brownian Motion: Fundamentals and Applications

Explores the fundamentals of Brownian motion, including particle positions and distribution functions.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Collisionless Boltzmann Equation

Explores the Collisionless Boltzmann equation, distribution functions, Jeans theorems, resonances, chaotic orbits, and equilibria in stellar systems.

Probability Distributions: Central Limit Theorem and Applications

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Do ImageNet Classifiers Generalize?

Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.

Convergence in Distribution

Covers the concept of convergence in distribution and its characterization using moments.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

Understanding ImageNet Classifiers

Explores the generalization of ImageNet classifiers and the challenges in achieving reliable machine learning models.

Introduction to Continuous Random Variables: Probability Distributions

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Generating Functions: Properties and Applications

Explores generating functions, Laplace transform, and their role in probability distributions.

Rare Events and Extreme Modeling

Explores rare event modeling, challenges in estimating probabilities with limited data, and the application of extreme value theory in various fields.

Learning in the Presence of Distribution Shifts: How does the Geometry of Perturbations Play a Role?

Explores learning challenges with distribution shifts and perturbation geometry, focusing on robust classifiers and natural variation modeling.

Stellar Systems Dynamics

Explores stellar systems dynamics, covering models, equations, and velocity distributions in spherical and cylindrical coordinates.

Ancient Land Management

Explores ancient land management practices, focusing on a land registry found in Orange, Provence, France.

Probability Distributions: Moments and Transformations

Explores the bijection between distribution functions and moment generating functions.