Lectures related to Decision analysis

Concept Selection and Tradespace Exploration

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

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.

Decision Tree Classification

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Supervised Learning: Formalization and Cost Functions

Covers the formalism for supervised learning and decision functions in classification problems.

Support Vector Machines: Soft Margin SVM

Introduces Soft Margin SVM, aiming to balance errors and margin width.

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Thermodynamic Properties: Equations and Models

Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.

Symbolic Representation of State Spaces

Delves into symbolic representation of state spaces using decision diagrams for high-level Petri nets, showcasing efficient encoding techniques and benchmark results.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Advanced Machine Learning: Feature Selection

Explores machine learning algorithms, feature selection techniques like FAST and BRIEF descriptors, and the limitations of deep learning.

Multi-Criteria Analysis: Investment Choice

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

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Support Vector Machines: Parameters, Solutions, and Boundaries

Explores SVM parameters, solutions, and decision boundaries, including the uniqueness of solutions and the impact of kernel width.

Geometric Insights on Deep Learning Models

Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.

Market Mechanisms: Crowdfunding, Prediction Markets, Quadratic Voting

Explores crowdfunding, prediction markets, and quadratic voting as market-based mechanisms.

Linear Models: Classification Basics

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Interpretable Machine Learning: Sparse Decision Trees and Interpretable Neural Networks

Explores the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks.

Distribution Estimation

Covers the estimation of distributions using samples and probability models.