Lectures related to Exploration Bias

Gradient Descent and Linear Regression

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

Supervised Learning with kNN: Regression Model

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

Generalized Linear Regression

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

Untitled

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Arbitrage in Real Estate Construction

Explores real estate arbitration, including staggered payments, interest calculations, and refurbishment costs.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Construction of Solutions by Dirichlet-Laplace Log in to Mediaspace to watch this video

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

Financial Decision Making: Cost-Benefit Analysis in Projects Log in to Mediaspace to watch this video

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 Log in to Mediaspace to watch this video

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

Architectural Processes: Non-linearity and Iteration Log in to Mediaspace to watch this video

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

Regularization in Machine Learning Log in to Mediaspace to watch this video

Introduces regularization techniques to prevent overfitting in machine learning models.

Gradient Descent: Linear Regression Log in to Mediaspace to watch this video

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Statistical Learning: Fundamentals Log in to Mediaspace to watch this video

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Gradient Descent Log in to Mediaspace to watch this video

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

Pizza Making Process Log in to Mediaspace to watch this video

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.