Model Evaluation

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Related lectures (31)

Page 3 of 4

Model Selection Criteria: AIC, BIC, Cp

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

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Data Representations and Processing

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Deep Learning: Overparameterization and Generalization

Explores overparameterization, generalization, overfitting, underfitting, and implicit regularization in deep learning models.

Overfitting, Cross-validation, Regularization

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

Model Selection and Evaluation

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

Supervised Learning in Asset Pricing

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.