Lectures related to Cross-validation & Regularization

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Overfitting, Cross-validation & Regularization

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Regularization in Machine Learning

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

Linear and Ridge Regression

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Machine Learning Fundamentals: Regularization and Cross-validation Log in to Mediaspace to watch this video

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

Cross-Validation: Techniques and Applications Log in to Mediaspace to watch this video

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

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.

Overfitting in Supervised Learning: Case Studies and Techniques Log in to Mediaspace to watch this video

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Model Complexity and Overfitting in Machine Learning Log in to Mediaspace to watch this video

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Regularization in Machine Learning Log in to Mediaspace to watch this video

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.