Learning Probability Distributions of Neuronal Activity Patterns - Max Entropy Models
Explore max entropy models for understanding neuronal activity patterns and probability distributions in biological systems.
Supervised Quantile Normalization for Matrix Factorization Using Optimal Transport
Explore advanced matrix factorization techniques using optimal transport for quantile normalization. Learn applications in dimensionality reduction and genomics from expert Marco Cuturi.
Probabilistic Machine Learning - Lecture 2
Dive into fundamental data science concepts and advanced computational techniques through comprehensive lecture materials from the University of Utah's graduate-level program.
Probabilistic Machine Learning - Lecture 3
Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical foundations and practical applications in modern data analysis.
Probability and Statistics: Understanding Independence - Lecture 4
Master the concept of independence in probability theory through detailed explanations, examples, and practical applications in statistical analysis and data science.
Exponential Family and Information Theory in Probability Distributions - Lecture 8
Dive into advanced statistical concepts including exponential families, multivariable Gaussian distributions, and information theory fundamentals for data science applications.
Discrete Distributions in R - Lecture 6
Master discrete distributions through hands-on practice in R, exploring probability concepts and statistical analysis techniques for working with categorical data.
Exponential and Normal Distribution with Sinusoidal Functions - Lecture 7B
Explore exponential and normal distributions while analyzing the complex function f(x) = (sin(1/x)+1)/1.5, gaining practical insights into probability theory and statistical analysis.
Probability and Statistics: Expectation of Random Variables - Lecture 8
Master probability concepts through discrete and continuous random variables, exploring Bernoulli, geometric, exponential, and normal distributions with practical examples and linearity principles.
Probability and Statistics: Joint Discrete Random Variables and Distributions - Lecture 9
Master probability concepts from exponential distribution to joint discrete random variables, including marginal and conditional probability calculations for comprehensive statistical analysis.
Expected Values in Random Variables - Lecture 9.5
Master the fundamentals of random variables and their expected values, exploring key concepts like independence and probability distributions in statistical analysis.
Probability and Statistics: A Comprehensive Review - Lecture 18
Master essential probability and statistics concepts, from basic probability and random variables to hypothesis testing and confidence intervals in this comprehensive semester review.
ML-Based Time Series Regression - Concepts from Demand Forecasting
Explore ML-based time series regression for demand forecasting in retail. Learn to optimize replenishment and pricing using AI for accurate predictions and automated decision-making at scale.
Minimum Entropy of a Log-Concave Random Variable with Fixed Variance
Delve into the mathematical analysis of log-concave random variables, exploring entropy minimization, channel capacities, and improved entropy power inequalities with practical applications.
Flip a coin. | Minnie Hwang | TEDxChadwick International School
If a coin lands, was it really 50/50? This Tedx-talk directly challenges the idea that our society must accept probabilities. Probabilities never described our world, it described our assumptions on it. So, it probability is more based on assumption, than it is on truth, to what extend can we really