Recommender Systems: Overview and Methods

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Related lectures (32)

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Bias-Variance Trade-Off

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

Data Representations & Processing

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

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.

Deep Learning: Designing Neural Network Models

Covers the design and optimization of neural network models in deep learning.

Specification Testing and Machine Learning

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Supervised Learning in Financial Econometrics

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Optimal Regularization Strength and Learning Curves

Covers loading datasets, understanding dimensions, learning curves, and the impact of regularization on overfitting.

Regression Trees and Ensemble Methods in Machine Learning

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