Lectures related to Support Vector Machines: Soft Margin SVM

Support Vector Machines: Exercises Solutions

Covers solutions to SVM exercises, discussing optimality conditions, decision functions, and parameter impacts.

Nonlinear SVM: Kernels and Dual Optimization

Explores transforming data with nonlinear maps, kernels, dual optimization, and interpreting SVM results.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Introduction to Supervised Learning and Decision Theory

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

Support Vector Machine Extensions: SVM, RVM, Transductive SVM

Explores SVM extensions, RVM, Transductive SVM, and support vector clustering in advanced machine learning.

Linear SVM derivation

Covers the derivation of Linear Support Vector Machine (SVM) and the Karush-Kuhn-Tucker (KKT) conditions.

Supervised Learning: Formalization and Cost Functions

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

Likelihood Ratio Test: Detection & Estimation

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

Support Vector Machines: Dual Formulation for Hard Margin

Explores the dual formulation of Support Vector Machines for hard margin classification.

Support Vector Machines: Interactive Class

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

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

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.

Optimisation in Energy Systems

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

Bit Error Rate and Receiver Sensitivity

Explores bit error rate and receiver sensitivity in optical communication systems, covering BER, receiver sensitivity, probability density functions, and error probability calculations.

Decision Tree Classification

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

Cross-Entropy and Regression Cost Functions

Explores cross-entropy in classification and various regression cost functions.

Thermodynamic Properties: Equations and Models

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

Support Vector Machines: Theory and Applications

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Structured Classifications: Decision Trees and Boosting

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

Ethics and Law of AI

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