Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Variable Selection Methods: Subset vs. Container Approaches
Graph Chatbot
Related lectures (32)
Previous
Page 3 of 4
Next
Generalized Linear Models
Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Sparse Regression
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Model Complexity and Overfitting in Machine Learning
Covers model complexity, overfitting, and strategies to select appropriate machine learning models.
Risk Estimation: Mallows' CL and Cp
Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.
Principal Component Analysis: Geometric Interpretation and Dimension Reduction
Explores Principal Component Analysis for dimension reduction and data representation in a new basis.
Overfitting in Supervised Learning: Case Studies and Techniques
Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
Model Selection Methods in Biostatistics
Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.
Graph Algorithms: Modeling and Traversal
Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.
Model Selection: Evaluation and Generalization
Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Regression Methods: Model Building and Inference
Covers analysis of variance, model building, variable selection, and function estimation in regression methods.