Gaussian Distribution Courses and Certifications

Weights & Biases

Math for Machine Learning - Exercises: Probability

Hands-on exercises exploring probability concepts in machine learning, including entropy, cross-entropy, and Gaussian distributions, with practical applications and insights.

• YouTube
• 44 minutes
• On-Demand
• Free Video

The Julia Programming Language

Random Variables as Types - Lecture 11

Explore random variables as types in Julia programming, covering Gaussian distributions, sampling, abstract types, and generic programming for probability distributions.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Shaw Talebi

Pareto, Power Laws, and Fat Tails: A Beginner's Guide to Non-Gaussian Statistics

Explore power laws, fat tails, and their impact on statistical analysis. Learn why traditional methods fall short and gain insights into analyzing extreme events in various domains.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Steve Brunton

The Chi Squared Distribution: The Square of the Normal Distribution

Explore the Chi-squared distribution, its relationship to Gaussian variables, and its applications in statistical comparisons of distributions.

• YouTube
• 13 minutes
• On-Demand
• Free Video

Steve Brunton

Variance and Standard Deviation

Master variance and standard deviation concepts to measure probability distribution spread with practical examples and mathematical derivations.

• YouTube
• 13 minutes
• On-Demand
• Free Video

Steve Brunton

Random Variables and Probability Distributions - From Basics to Distributions

Explore the fundamentals of random variables, from basic coin flip examples to probability distributions, with clear explanations of their role in statistical analysis and calculus applications.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Steve Brunton

The Standard Unit Normal and Probability Computations

Master probability calculations using the standard unit normal distribution, including mean-centering, scaling, and computing percentiles through real-world examples and practical applications.

• YouTube
• 17 minutes
• On-Demand
• Free Video

Artem Kirsanov

What Textbooks Don't Tell You About Curve Fitting

Discover the probabilistic interpretation behind linear regression, exploring how least squares objectives arise from maximizing data probability and how different priors lead to various regularization techniques.

• YouTube
• 18 minutes
• On-Demand
• Free Video

Discover AI

Diffusion Transformer and Rectified Flow in Stable Diffusion 3

Dive into the mechanics of Diffusion Transformers, exploring noise distribution, latent space dimensionality, and the innovative Rectified Flow technology powering next-gen AI image generation.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Steve Brunton

Maximum Likelihood Estimation Example - Fitting a Normal Distribution with Data

Master maximum likelihood estimation by fitting normal distributions to data through joint PDFs, log-likelihood calculations, and parameter optimization for mu and sigma.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Some Applications of Variational Techniques in Stochastic Geometry III

Explores probabilistic bounds on Poisson space, measuring Gaussian distance for two-scale stabilization elements. Discusses quantitative CLTs for weakly stabilizing functionals and novel bounds for strongly stabilizing ones.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Fields Institute

Private Learning of Gaussians and Their Mixtures

Explore private learning techniques for Gaussian distributions and mixtures, focusing on robust methods, hypothesis selection, and high-dimensional cases.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Centre de recherches mathématiques - CRM

Adaptive Bayesian Predictive Inference

Explore adaptive Bayesian predictive inference for high-dimensional sparse mean estimation, examining sparsity priors and their performance in predictive density estimation under Kullback-Leibler loss.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Valence Labs

Linear Causal Representations from Interventions under General Nonlinear Mixing

Explore causal representation learning from latent interventions in nonlinear settings. Uncover geometric structures, analyze precision matrices, and evaluate a contrastive algorithm for identifying latent variables.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Institut des Hautes Etudes Scientifiques (IHES)

Covariance and Subspace Inference: Handling Robustness, Variability and Heterogeneity

Explore covariance matrix inference and PCA for non-regular data, covering mixed effects models, Gaussian assumptions, outlier handling, and applications in radio-interferometry calibration and imaging.

• YouTube
• 38 minutes
• On-Demand
• Free Video