Machine Learning
Comprehensive 7-day exploration of key machine learning algorithms, from regression techniques to clustering methods, enhancing data science skills through live interactive sessions.
General Linear Models - Regression
Comprehensive exploration of linear regression models, covering simple and multiple regression techniques, statistical inference, model diagnostics, and advanced topics for in-depth statistical analysis.
Predicting Rare Events Using Ordinal Regression - From Purchases to Diseases
Dive into advanced ordinal regression techniques for predicting rare events like purchases and diseases, with practical examples and implementation guidance.
Introduction to Lasso and Ridge Regression Models in R
Master linear regression regularization techniques through Lasso and Ridge methods in R, focusing on preventing overfitting and improving model reliability for more accurate predictions and estimations.
Regularization Part 1 - Ridge (L2) Regression
Learn Ridge Regression to prevent overfitting, solve unsolvable equations, and optimize models. Covers main ideas, details, applications for various data types and models, and usage with limited data.
Pretraining Task Diversity and the Emergence of Non-Bayesian In-Context Learning for Regression
Explores how task diversity in pretraining affects transformers' in-context learning abilities for regression, revealing a threshold beyond which models can solve novel tasks and outperform Bayesian estimators.
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.
Model Invariants and Functional Regularization
Explore model invariants and functional regularization in quantitative finance. Learn about regression, MLE, Bayesian estimation, and new approaches to improve model robustness and interpretability in data analysis.
Handling Multicollinearity with Ridge Regression in Python
Implement Ridge Regression using Python and sklearn to handle multicollinearity in data analysis.
Fast and Accurate Probabilistic Time Series Classification
Explore fast and accurate probabilistic time series classification using MINIROCKET technology, achieving near-state-of-the-art performance with significantly reduced computational costs compared to existing methods.
Nonlinear Regression and Regularization in Data Mining - Spring 2023
Master nonlinear regression concepts, from polynomial mappings to regularization techniques, while learning practical implementation in Python and understanding cross-validation for model optimization.