Lectures related to Linear Programming: Convex Hull

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Validation and k-Nearest Neighbors Method

Introduces supervised learning concepts and the k-Nearest Neighbors method for classification and regression tasks.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Linear Regression: Statistical Inference and Regularization

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

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Real Estate Data Analysis: Descriptive Statistics

Explores real estate data analysis through descriptive statistics, correlation, regression, and forecasting methods.

Supervised Learning with kNN: Regression Model

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

Loss Functions and Optimization

Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.

Optimization Problems: Path Finding and Portfolio Allocation Log in to Mediaspace to watch this video

Covers optimization problems in path finding and portfolio allocation.

Linear Programming Basics Log in to Mediaspace to watch this video

Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.

Kernel Regression: K-nearest Neighbors Log in to Mediaspace to watch this video

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Cutset Formulation: MST Problem Log in to Mediaspace to watch this video

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Convex Optimization: Nonlinear Dimensionality Reduction Log in to Mediaspace to watch this video

Explores convex optimization in nonlinear dimensionality reduction, showcasing practical applications in signal processing and regression tasks.

Gradient Descent: Optimization Techniques Log in to Mediaspace to watch this video

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Decision Trees: Regression and Classification Log in to Mediaspace to watch this video

Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Linear and Weighted Regression: Optimal Parameters and Local Solutions Log in to Mediaspace to watch this video

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.