Computing Climate Extreme Events and Extremes of Production of Renewable Energy
Explore rare events algorithms for calculating climate extremes and renewable energy production anomalies. Learn advanced computational techniques for analyzing environmental data.
Bivariate Analysis in R
Learn bivariate analysis techniques in R, including t-tests, ANOVA, chi-square, correlations, and linear regression. Gain practical skills for data science and statistics using RStudio.
R Tutorials
Comprehensive R tutorials covering data management, analysis, and visualization, with hands-on exercises using real datasets and practical applications in statistics and data science.
R for Beginners
Comprehensive R tutorials for beginners, covering installation, data manipulation, statistical analysis, and visualization. Hands-on approach with downloadable data files for practical learning.
Poisson Regression with Tidymodels for Package Vignette Counts
Learn to fit zero-inflated Poisson regression models using tidymodels to analyze R package vignette counts, with practical examples and code demonstrations.
Quick Intro to R
Learn to install and use R programming language, covering basics like data creation, functions, statistics, and visualizations in this quick introduction.
Understanding Latency: Pitfalls, Lessons and Tools - Philly ETE 2014
Explore common pitfalls in latency measurement, learn techniques for accurate reporting, and discover open-source tools to improve confidence in application responsiveness analysis.
Survival Analysis on Cancer Data - RStudio Tutorial
Aprenda análise de sobrevivência em dados de câncer usando R. Tutorial prático sobre pipeline de análise, uso de dados TCGA, expressão gênica e modelagem preditiva para pesquisa biomédica.
Running Basic Statistical Analysis in R
Learn to perform essential statistical analyses in R, including correlation, linear modeling, t-tests, and heatmaps. Ideal for beginners seeking to leverage R's powerful command-line interface for data analysis.
Python for Finance - Introduction to Stock Market Analysis
Learn to analyze stock market data using Python, covering Numpy, Pandas, Matplotlib, data manipulation, statistical calculations, and visualization techniques for financial analysis.
Introduction to R Programming for Excel Users
Learn to apply Excel skills in R programming for data analysis, visualization, and advanced techniques. Discover how Excel concepts translate to R, enabling powerful data manipulation and insights.
Provably Fair and Robust Machine Learning
Explore cutting-edge methods for building fair and robust machine learning systems, covering tabular and vision data alongside deterministic and statistical certification approaches.
Introducing Two Criteria for Future Data Prediction with ML Models
Explore transparent evaluation methods for ML models through new criteria linking database characteristics to machine learning metrics, focusing on future data prediction and unbiased assessment.
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Delve into stochastic combinatorial semi-bandits with a focus on risk-controlled online decision making, exploring algorithms for optimal subset selection while maintaining variance constraints.
Principal Component Analysis - PCA Clearly Explained - 2015
Clear explanation of Principal Component Analysis, covering dimensions, variance, loading scores, and key concepts for interpreting PCA plots in RNA-seq data analysis.
Asiacrypt 2022 Practical Cryptography 1
Explore practical cryptography concepts including encryption, ciphers, and boomerang attacks in this comprehensive tutorial on advanced cryptographic techniques.
Price Optimisation - From Exploration to Productionising - David Adey, PhD & Alexey Drozdetskiy, PhD
Explore dynamic price optimization using machine learning, from data analysis to cloud deployment. Learn about feature encoding, model selection, efficiency improvements, and practical implementation strategies for revenue growth.
What Are the Statistical Limits of Offline Reinforcement Learning With Function Approximation?
Explore statistical limitations in offline reinforcement learning with function approximation, covering realizability, coverage, policy evaluation, and practical considerations.
Nearest Neighbors I
Explore nearest neighbor algorithms in machine learning, covering regression, classification, consistency, and convergence rates in metric spaces. Gain insights into statistical learning frameworks and smoothness conditions.
Dragon-Kings, Black-Swans and Prediction - Lorenz Lecture
Explore extreme events, their predictability, and impact on complex systems through statistical physics and nonlinear dynamics in this insightful lecture by D. Sornette.
Privacy Preserving Aggregate Statistics via Boolean Shares - Part 3
Privacy-preserving aggregate statistics using Boolean shares, enhancing the Prio system for improved data collection and analysis while maintaining user privacy.