Linear Programming: Convex Hull

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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.