Support Vector Machines: Soft Margin SVM

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Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Optimal Decision Analysis

Explores strong duality, complementary slackness, economic interpretation, and stochastic problem scenarios in linear programming.

Concept Selection and Tradespace Exploration

Covers decision analysis, concept selection methods, non-dominance, and optimization in system design.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Distribution Estimation

Covers the estimation of distributions using samples and probability models.

Decision and regression trees

Explores decision and regression trees, impurity measures, learning algorithms, and implementations, including conditional inference trees and tree pruning.

Multi-Criteria Analysis: Investment Choice

Covers multi-criteria decision analysis for investment choice and sustainability, including challenges in criteria selection and scoring methods.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Decisions under Risk and Uncertainty: Behavioral Economics Insights

Delves into decision-making under risk, bounded rationality, prospect theory, and framing, with insights from behavioral economics.

Optimization Methods: Theory Discussion

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