MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".

Machine-Learning-Regression

This repository includes some detailed proofs of "Bias Variance Decomposition for KL Divergence".

The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

This is a simple python example to demonstrate bias variance

The Bias-Variance Tradeoff Visualization project provides an interactive tool to understand the bias-variance tradeoff in machine learning models. It visually demonstrates how different models perform on training and validation datasets, helping users grasp the concepts of overfitting and underfitting.

Bias variance experiment from Learning from Data. Problem 2.24, p. 75.

Explanation of the Bias Variance Tradeoff in Machine Learning

Performing polynomial regression of varying degrees on data affected by white and Poisson noise, evaluating the model performance based on MSE loss and the bias-variance trade-off.

Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning

Deep Learning project about the design and training of a model for Image Classification

Bias and Variance Tradeoff for debugging

This repository contains a generalized regression analysis problem solved from scratch, using only the Numpy library.

Machine learning assignments covering regression, classification, neural networks, adversarial examples, and real-time emotion detection using Python. Includes theoretical insights and practical implementations.

This repository has been created just for warm-up in machine learning and there are my simulation files of UT-ML course HWs.

This project focuses on developing and training supervised learning models for prediction and classification tasks, covering linear and logistic regression (using NumPy & scikit-learn), neural networks (with TensorFlow) for binary and multi-class classification, and decision trees along with ensemble methods like random forests and boosted trees

Mithilfe von Machine Learning und Open Data zu Unfällen in Berlin (2018-2021) beantworten wir folgende Frage: Was sind die wichtigen Faktoren/Einflüsse auf Unfallgefahr? Und wie gut lässt sich damit die Unfallschwere überhaupt vorhersagen?

Collection of all assignments given in the Machine, Data and Learning Course (CS7.301).

Estimating the parametric complexity (minimum description length) of binary classifiers.