Lectures related to Decision model

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

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.

Perceptual Decision Making: Neuronal Dynamics

Explores competitive dynamics in perceptual decision making and neuronal responses in visual cortex V5/MT.

Decision Theory: Risk and Hypothesis Testing

Covers decision theory, risk functions, and hypothesis testing in statistical inference.

Thermodynamic Properties: Equations and Models

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

Introduction to Supervised Learning and Decision Theory

Covers supervised learning, decision theory, risk minimization, and goal achievement.

Ethics and Law of AI

Delves into ethics and law in AI, focusing on values, normative ambitions, and ethical challenges.

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.

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.

Optimisation in Energy Systems

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

Structured Classifications: Decision Trees and Boosting

Explores decision trees, overfitting elimination, boosting techniques, and their practical applications in predictive modeling.

Decision Theory: Risk and Inference

Explores decision theory, risk functions, and inference in statistical analysis.

Statistical Theory: Decision Theory Framework

Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.

Concept Selection and Tradespace Exploration

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

Assessing Intervention Characteristics

Covers assessing intervention characteristics, analyzing implementation problems, and strategies for addressing barriers.

Hypothesis Testing: State of Nature

Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.

Multi-Criteria Analysis: Investment Choice

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

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.