Statistical Rethinking 2022 - Overfitting
Explore overfitting in statistical modeling, covering cross-validation, regularization, information criteria, and robust regression techniques for improved predictive accuracy.
Algorithms for Heavy-Tailed Statistics - Regression, Covariance Estimation, and Beyond
Explore advanced statistical techniques for handling heavy-tailed data, including regression and covariance estimation, with a focus on SOS assumptions and median of means framework.
Machine Learning Basics: A Speedrun - IPAM at UCLA
Speedrun through machine learning fundamentals, covering key concepts like inductive bias, regularization, and kernel methods for both linear and nonlinear regression.
Simple and Basic Evaluation Metrics for Regression
Learn essential evaluation metrics for regression problems, including MAE, MSE, RMSE, RMSLE, and R2 score, with practical implementation in Python.
Model Explanation and Prediction Exploration Using Spark ML
Explore techniques for explaining non-linear models, showing feature contributions, and performing what-if analysis using Spark ML. Implement these methods to enhance model interpretability and enable interactive prediction exploration.
Benefits of Saying I Don't Know When Analyzing and Modeling the Climate System With ML - Elizabeth Barnes
Explore the benefits of acknowledging uncertainty in climate system analysis using machine learning. Learn about state-dependent predictability and controlled abstention networks for improved modeling accuracy.
Day-to-Day Testing of Linux 'Next' Kernel Branch
Explore daily testing of Linux 'next' kernel branch, including system setup, regression identification, reporting, and automated bisection. Learn practical tips for finding and fixing kernel issues.
Interacting Particle Systems for Expectation Maximization in Latent Variable Models
Explore a novel interacting particle system for EM optimization in latent variable models, featuring Langevin diffusion analysis and non-asymptotic concentration bounds for maximum likelihood estimation.
Hitting the High-Dimensional Notes - An ODE for SGD Learning Dynamics in High-Dimensions
Explore stochastic optimization algorithms in high dimensions, analyzing dynamics and performance of SGD and momentum methods using a framework inspired by random matrix theory.
Seeing Faces in Things: A Model and Dataset for Pareidolia
Explore face pareidolia through computer vision, examining human vs. machine detection abilities and proposing a statistical model for general object pareidolia in images.
Machine Learning Models for Single Cell and Regulatory Genomics - CGSI 2023
Explore machine learning models for single-cell and regulatory genomics, focusing on recent research and applications in computational biology.
Maximum Entropy and Species Distribution Modeling - 2007 - Lecture
Explore maximum entropy methods for species distribution modeling, covering theoretical guarantees, experimental tests, and applications in conservation biology.
Probabilistic Programming in Python - Conf42 Python 2024
Explore Bayesian statistics, probabilistic modeling, and MCMC in Python. Learn key concepts and practical applications through demonstrations and workflow explanations.
Machine Learning for Petrophysics
Explore machine learning applications in petrophysics with expert Osama Osman, covering key concepts and practical implementations for oil and gas industry professionals.
Scala DSLs and Probabilistic Programming
Explore ScalaStan, a type-safe DSL for statistical modeling with Stan, enabling programmatic manipulation and generation of Stan programs within Scala for efficient Bayesian inference and data analysis.
Automated Performance Budgeting into Your Pipeline with Sitespeed.io - JSConf US
Explore automated performance budgeting using sitespeed.io, focusing on front-end optimization for large organizations and the future of performance testing tooling.
Essentials of Math Modeling - Introduction to Regression and Statistical Ideas
Explore linear regression, data transformations, and statistical concepts in mathematical modeling. Learn MATLAB examples, sensitivity analysis, and problem-solving techniques for real-world applications.
Environmental Factors in Indoor Navigation Based on Real-World Trajectories of Blind Users
Analyzing real-world trajectories of blind users to improve indoor navigation systems, focusing on path-following behavior, environmental factors, and implications for future design.
All Work and No Play? Conversations with a Question-and-Answer Chatbot in the Wild
Exploring user interactions with HR chatbots, analyzing conversational patterns, and discussing design implications for adaptive conversational agents that balance functionality and playful engagement.
Probabilistic Programming in Python
Learn probabilistic programming in Python using PyMC3 for flexible statistical modeling, automated parameter estimation, and uncertainty quantification in data science and scientific research.
Data Analytics Explained for Business Leaders
Demystifying data analytics for business leaders, bridging the gap between technical and strategic understanding to drive innovation, productivity, and informed decision-making.