Exploration Bias

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Related lectures (30)

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

Construction of Solutions by Dirichlet-Laplace

Explores the construction of solutions by Dirichlet-Laplace in general domains.

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Financial Decision Making: Cost-Benefit Analysis in Projects

Covers financial decision making through cost-benefit analysis in public projects, focusing on investment viability and the implications of interest rates.

Gradient Descent on Two-Layer ReLU Neural Networks

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Generalized Linear Regression

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

Architectural Processes: Non-linearity and Iteration

Covers the non-linear nature of architectural processes and the importance of iteration in project development.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

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