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Related lectures (48)

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.

Polynomial Regression: Basics and Regularization Log in to Mediaspace to watch this video

Covers the basics of polynomial regression and regularization to prevent overfitting.

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.

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.

Polynomial Regression and Gradient Descent Log in to Mediaspace to watch this video

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

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.

Kernel Methods in Machine Learning: Kernel Regression and SVM Log in to Mediaspace to watch this video

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

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Model Evaluation Log in to Mediaspace to watch this video

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Understanding Data Attributes Log in to Mediaspace to watch this video

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

General Linear Model: Model Selection Log in to Mediaspace to watch this video

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Regression: Interactive Lecture Log in to Mediaspace to watch this video

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Machine Learning: Supervised and Unsupervised Learning Techniques Log in to Mediaspace to watch this video

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression Log in to Mediaspace to watch this video

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation Log in to Mediaspace to watch this video

Explores over-learning, generalization, and under-learning in machine learning models.

Regression Again: Exercise 3.1 Log in to Mediaspace to watch this video

Covers exercises on regression, including linear and polynomial regression, high dimensions, and real data analysis.

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