Lasso/Elastic Net linear and generalized linear models
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Updated
Oct 6, 2024 - Julia
Lasso/Elastic Net linear and generalized linear models
Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.
Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.
Andrew Ng's Machine Learning Course
Housing price prediction using Regularised linear regression
This repository corresponds to the course "Statistical Learning Theory" taught at the School of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Joint Interuniversity Master's Program under the instructor Pedro Delicado
This is the implementation of the five regression methods Least Square (LS), Regularized Least Square (RLS), LASSO, Robust Regression (RR) and Bayesian Regression (BR).
Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.
Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.
Solutions to Coursera's Intro to Machine Learning course in python
Regularized logistic regressions with computational graphs
This repository contains several machine learning projects done in Jupyter Notebooks
A Mathematical Intuition behind Linear Regression Algorithm
Course work for Machine Learning Course by Stanford University on Coursera
I developed a function to perform regularized linear and Gaussian basis functions for regression. Some dataset from the UCI machine learning repository were used to validate the function.
SparseStep: Approximating the Counting Norm for Sparse Regularization
High dimensional linear regression with missing via adaptive SLOPE
The course studies fundamentals of distributed machine learning algorithms and the fundamentals of deep learning. We will cover the basics of machine learning and introduce techniques and systems that enable machine learning algorithms to be efficiently parallelized.
High Throughput Light Weight Regularized Regression Modeling for Molecular Data
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