Loss function

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Related lectures (129)

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Related lectures (129)

Page 5 of 13

Introduction to Supervised Learning and Decision Theory

Covers supervised learning, decision theory, risk minimization, and goal achievement.

Generative Adversarial Networks

Explores Generative Adversarial Networks, equilibrium, divergences, variants, and diffusion models.

Quantum Sensing: Parameter Estimation and Bounds

Covers parameter estimation in quantum metrology, Quantum Fisher Information, Ramsey interferometry, entanglement, and noise impact on NooN states.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Linear Binary Classification

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.

Law of Large Numbers, Statistics

Covers the Law of Large Numbers in Statistics and methods for deriving estimators and maximum likelihood.

Robust Regression in Genomic Data Analysis

Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.

Stochastic Gradient Descent: Optimization and Convergence Analysis

Explores Stochastic Gradient Descent for non-strongly convex functions and the impact of step size on convergence.

Stochastic Optimization: Algorithms and Methods

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.