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.

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.

Explanation of the Bias Variance Tradeoff in Machine Learning

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

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

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

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

Bias and Variance Tradeoff for debugging

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?

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

Machine Learning programs in R

A python code for demonstration of Bias-Variance TradeOff concept in Machine Learning

Single Layer Perceptrons (SLPs) and Multi-Layer Perceptrons (MLPs) from scratch, only with numpy, for classification and regression. MLPs with Keras for time-series prediction.