Discrete choice and machine learning: two complementary methodologies

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Model Selection and Evaluation

Discusses the experimental framework for selecting and evaluating supervised learning models to prevent overfitting.

Machine Learning Fundamentals

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Machine Learning Fundamentals: Overfitting and Regularization

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Bias-Variance Trade-Off

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.