Gaussian Distribution Courses and Certifications

Financial Analytics in Google Sheets

Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Mixture Models in R

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Probability and Random Processes

3 reviews

Explore random variables, probability distributions, and stochastic processes through intuitive explanations and practical applications in parameter estimation and noisy measurements.

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

NPTEL;  IIT Madras

Statistical Mechanics

Comprehensive exploration of probability, statistical ensembles, and quantum mechanics, covering discrete and continuous distributions, entropy, and applications to physical systems.

• YouTube
• 1 day 6 hours
• On-Demand
• Free Video

Nonadditive Entropies and Statistical Mechanics at the Edge of Chaos - Cornerstones

Explore nonadditive entropies and statistical mechanics at chaos' edge. Delve into Boltzmann-Gibbs entropy, Q-logarithm, and complex networks, challenging traditional thermodynamic principles.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

University of Tübingen

Probabilistic Machine Learning

Comprehensive lecture series on probabilistic machine learning, covering Bayesian paradigms, statistical concepts, and deep learning connections for master's-level computer science students.

• YouTube
• 1 day 16 hours
• On-Demand
• Free Online Course

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

Weights & Biases

Probability - Math for Machine Learning

Explore core probability concepts for machine learning, including surprises, loss functions, and Gaussians. Gain insights into the mathematical foundations of ML algorithms.

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

Richard McElreath

Statistical Rethinking 2023 - Geocentric Models

Explore geocentric models in statistical thinking, covering Gaussian distributions, workflows, generative models, and analysis techniques for improved statistical reasoning.

• YouTube
• 1 hour 14 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

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

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

BSS Physics School

Driven Quantum Dynamics III - Lecture 3

Explore quantum dynamics, focusing on localization, Fourier expansion, and chaos. Gain insights into stable localization, delocalization, and Anderson localization.

• YouTube
• 1 hour 31 minutes
• On-Demand
• Free Video

USC Probability and Statistics Seminar

Sample Amplification: Increasing Dataset Size Even When Learning is Impossible

Explore sample amplification techniques to generate larger datasets from limited samples, even when learning the original distribution is impossible.

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

UofU Data Science

Probabilistic Graphical Models and Maximum Likelihood Estimation - Lecture 13

Dive into probabilistic graphical models, exploring likelihood estimation, Gaussian models, and network structures while learning to analyze real-world data using conditional independence concepts.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video