Home

Data analysis

Formal sciences
Statistics
Data analysis

Related lectures (31)

About
Privacy
Disclaimer

Graph Chatbot

Page 2 of 4

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Multivariate Time Series: Cointegration & Forecasting

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Machine Learning Fundamentals: Regularization and Cross-validation

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Linear Regression Basics

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Linear Regression: Basics

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.