Lectures related to Regression: High Dimensions

Linear Regression: Statistical Inference and Regularization

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

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Nonlinear ML Algorithms

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

Introduction to Machine Learning: Linear Models

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

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Linear Regression: Basics and Applications

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Overfitting, Cross-validation, Regularization

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

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

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.

Classification Algorithms: Generative and Discriminative Approaches Log in to Mediaspace to watch this video

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Linear Regression: Foundations and Applications Log in to Mediaspace to watch this video

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Understanding Data Attributes Log in to Mediaspace to watch this video

Covers the analysis of various data attributes and linear regression models.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

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

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Linear Models: Part 1 Log in to Mediaspace to watch this video

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

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.