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

Machine-Learning-Regression

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

This is a simple python example to demonstrate bias variance

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

Explanation of the Bias Variance Tradeoff in Machine Learning

Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour

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.

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

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

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.

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.