Lectures related to Ordinal regression

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.

Support Vector Regression: Kernel Tricks

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

Logistic Regression: Probabilistic Interpretation

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Support Vector Regression: Principles and Optimization

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Inference: Poisson Regression

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.

Inference: Model Checking

Covers iterative weighted least squares, generalized linear models, and model checking.

Regression Models: Performance and Evaluation

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

Linear and Weighted Regression: Optimal Parameters and Local Solutions

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Support Vector Regression: Nu-SVR and RVR

Explores advanced topics in machine learning, focusing on SVR extensions and hyperparameter optimization, including Nu-SVR and RVR.

Binary Responses: Link Functions and GLMs

Explores link functions for binary responses and the impact of sparseness on model interpretability.

Regression: Interactive Lecture

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Linear Regression: Mean-square-error Inference

Covers the MSE problem in linear regression models, focusing on the optimal estimator and data fusion methods.

Modern Regression: Overdispersion and Model Assessment

Explores overdispersion, model assessment, and regression techniques for count data.

Why Standard ML is Not Sufficient: Learning and Adaptive Control

Delves into the challenges of using standard ML for stable robot control.

Convex Optimization: Examples of Convex Functions

Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Comparison Across Methods: GMR vs SVR

Compares GMR and SVR in machine learning, discussing their similarities, differences, and hyperparameters.

Support Vector Regression: Recap and Convex Optimization

Covers the recap of Support Vector Regression with a focus on convex optimization and its equivalence to Gaussian Process Regression.

Statistical Physics for Optimization & Learning

Covers statistical physics tools for optimization, learning, graph coloring, recommendation systems, and neural networks.