Machine-Learning-Regression

This is a simple python example to demonstrate bias variance

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/)

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

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.

Explanation of the Bias Variance Tradeoff in Machine Learning

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

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.

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.

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?

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

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

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

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

Machine Learning programs in R

TLDR: Generic Algorithms, Decision Trees, Value Iteration, POMDPs, Bias-Variance. Data preprocessing using statistical techniques and visualization is crucial to understand and analyze the data before utilizing them to train a machine learning model. Several fundamental techniques for preprocessing are presented here.