Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Regression Methods: Model Building and Inference
Graph Chatbot
Related lectures (49)
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Regression: Linear Models
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Linear Regression: Estimation and Inference
Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.
Linear and Logistic Regression
Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.
Gaussian Mixture Regression: Modeling and Prediction
Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.
Linear Models: Least Squares
Explores linear models, least squares, Gaussian vectors, and model selection methods.
Linear Regression: Statistical Inference Perspective
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Regression Methods: Model Building and Inference
Log in to Mediaspace to watch this video
Covers analysis of variance, model building, variable selection, and function estimation in regression methods.
Regression Methods: Model Building and Diagnostics
Log in to Mediaspace to watch this video
Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.
Regression: Simple and Multiple Linear
Log in to Mediaspace to watch this video
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Modern Regression: Overdispersion and Model Assessment
Log in to Mediaspace to watch this video
Explores overdispersion, model assessment, and regression techniques for count data.
Supervised Learning: Regression Methods
Log in to Mediaspace to watch this video
Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.
Inference and Mixed Models
Log in to Mediaspace to watch this video
Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.
Regression Methods: Spline Smoothing
Log in to Mediaspace to watch this video
Covers regression methods focusing on spline smoothing and penalised fitting to balance data fidelity and smoothness.
Modern Regression: Spring Barley Data
Log in to Mediaspace to watch this video
Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.
Nonparametric Regression: Kernel-Based Estimation
Log in to Mediaspace to watch this video
Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.
Data-Driven Modeling: Regression
Log in to Mediaspace to watch this video
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Linear Regression and Logistic Regression
Log in to Mediaspace to watch this video
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Linear Regression: Basics and Estimation
Log in to Mediaspace to watch this video
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Previous
Page 1 of 3
Next
