Lectures related to Linear Programming: Convex Hull

Optimization Problems: Path Finding and Portfolio Allocation

Covers optimization problems in path finding and portfolio allocation.

Linear Programming: Weighted Bipartite Matching

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

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Linear Programming Basics

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

Neural Networks: Multilayer Perceptrons

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

Kernel Regression: K-nearest Neighbors

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

Cutset Formulation: MST Problem

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

Machine Learning Basics: Supervised Learning

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

Convex Optimization: Nonlinear Dimensionality Reduction

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

Gradient Descent: Optimization Techniques

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

Decision Trees: Regression and Classification

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

Regression Trees and Ensemble Methods in Machine Learning

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

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

Optimal Decision Making: Sensitivity Analysis

Covers sensitivity analysis in linear programming, focusing on optimal solutions and their sensitivities to changes.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Validation and k-Nearest Neighbors Method

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

Optimisation in Energy Systems

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Linear Models: Part 1

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Kernel Methods and Regression

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