机器学习-Coursera-吴恩达- python+Matlab代码实现

MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and Large-Scale Test Problems" that will be published in Numerical Algorithms, 2018.

Simple frequency domain full-waveform inversion (FWI) regularized by Sobolev space norm

My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.

Computational Ultrasound Imaging Toolbox for MATLAB

Machine learning coursera by Andrew Ng

Denoising/reconstruction of piecewise constant signals using the L1TV model

Controlled importance-weighted cross-validation

Codes for the paper "Semi-Supervised Feature Learning for Improving Writer Identification" in Information Sciences, 2019

MVPA tutorial - Rogers lab brain imaging unit

PDE-based vector-valued image regularization routine.

Software supporting "Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography" publication in Inverse Problems journal

This repository contains projects from Andrew NG's Machine Learning course at Coursera

Code for the paper E. Raninen, D. E. Tyler and E. Ollila, "Linear pooling of sample covariance matrices," in IEEE Transactions on Signal Processing, Vol 70, pp. 659-672, 2022, doi: 10.1109/TSP.2021.3139207.

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

Code for the paper E. Raninen and E. Ollila, "Bias Adjusted Sign Covariance Matrix," in IEEE Signal Processing Letters, vol. 29, pp. 339-343, 2022, doi: 10.1109/LSP.2021.3134940.

Codes used for breast cancer margin assessment in OCT images using DNNs

A modified version of the historical MATLAB code MELT additionally enabling tail-fitting on lifetime spectra consisting of distributed characteristic lifetimes using Maximum Entropy for optimization

Machine Learning with MATLAB

Regularization parameter estimation under covariate shift