Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

Machine learning algorithms in Dart programming language

A simple machine learning framework written in Swift 🤖

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

次元期权应征面试题范例。

A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

🤠 📿 The Highly Adaptive Lasso

A repository for a machine learning project about developing a hybrid movie recommender system.

Analyzes weightlifting videos for correct posture using pose estimation with OpenCV

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

An R package for modern methods for non-probability surveys

TwitPersonality: Computing Personality Traits from Tweets using Word Embeddings and Supervised Learning

Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) …

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and s…