Loss function

Formal sciences
Statistics
Statistical inference
Mathematical statistics

Related lectures (129)

About
Privacy
Disclaimer

Related lectures (129)

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

Classification Problems: Overview and Loss Functions

Covers classification problems and various loss functions used in machine learning.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Growth rate and uniform convergence

Explores growth rate, uniform convergence, PAC learning, and distribution learning challenges.

Non-Parametric Estimation: Kernel Density Estimators

Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.

Word Embedding Models: Optimization and Applications

Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.

Risk Estimation: Mallows' CL and Cp

Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.

Quantile Regression: Linear Optimization

Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.

Bayesian Inference: Part 2

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Mathematics of Data: Models and Estimators

Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.

Page 6 of 13