Matlab code of machine learning algorithms in book PRML

Matlab Coding homework for Machine Learning

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Matlab files with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz http://www.databookuw.com/

ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

Complete solutions for exercises and MATLAB example codes for "Machine Learning: A Probabilistic Perspective" 1/e by K. Murphy

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

This repository collates a number of MATLAB examples demonstrating Scientific Machine Learning (SciML) and Physics Informed Machine Learning.

Matlab implementations of Gaussian processes and other machine learning tools.

Matlab Notebook for visualizing random matrix theory results and their applications to machine learning

出版书籍《机器学习入门到实践——MATLAB实践应用》一书中的实例程序。涉及监督学习，非监督学习和强化学习。(code for book "Machine Learning Introduction & action in MATLAB")

Python implementation of the programming assignment from Machine Learning class on Coursera, which is originally implemented in Matlab/Octave.

Source Code for 'MATLAB Machine Learning' by Michael Paluszek and Stephanie Thomas

Matlab code for S. Theodoridis' "Machine Learning: A Bayesian and Optimization Perspective" (2015).

Some notes on machine learning algorithms, mostly in Matlab format.

Matlab implementation of Machine Learning algorithms

Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples. There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB). Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.

Source Code for 'MATLAB Machine Learning Recipes' by Michael Paluszek and Stephanie Thomas

it contains all the MATLAB demo code associated with my machine learning notes

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

Adaptive Beamforming techniques can be enhanced using Machine Learning Algorithms.

Matlab implementation of Extreme Learning Machine and variants

Curated collection of AI prompts for MATLAB development - enhance your workflow with MATLAB Copilot, GitHub Copilot, Claude, Cursor, and other AI coding assistants. Includes prompts for Live Scripts, signal processing, machine learning, control systems, and more.

The matlab code for "Change Detection from Synthetic Aperture Radar Images Based on Neighborhood-Based Ratio and Extreme Learning Machine", JARS 2016

# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos: Octave/Matlab Tutorial - [X] Quiz: Octave/Matlab Tutorial - [X] Programming Assignment: Linear Regression ### Week 3 - [X] Videos: Logistic Regression - [X] Quiz: Logistic Regression - [X] Videos: Regularization - [X] Quiz: Regularization - [X] Programming Assignment: Logistic Regression ### Week 4 - [X] Videos: Neural Networks: Representation - [X] Quiz: Neural Networks: Representation - [X] Programming Assignment: Multi-class Classification and Neural Networks ### Week 5 - [X] Videos: Neural Networks: Learning - [X] Quiz: Neural Networks: Learning - [X] Programming Assignment: Neural Network Learning ### Week 6 - [X] Videos: Advice for Applying Machine Learning - [X] Quiz: Advice for Applying Machine Learning - [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance - [X] Machine Learning System Design - [X] Quiz: Machine Learning System Design ### Week 7 - [X] Videos: Support Vector Machines - [X] Quiz: Support Vector Machines - [X] Programming Assignment: Support Vector Machines ### Week 8 - [X] Videos: Unsupervised Learning - [X] Quiz: Unsupervised Learning - [X] Videos: Dimensionality Reduction - [X] Quiz: Principal Component Analysis - [X] Programming Assignment: K-Means Clustering and PCA ### Week 9 - [X] Videos: Anomaly Detection - [X] Quiz: Anomaly Detection - [X] Videos: Recommender Systems - [X] Quiz: Recommender Systems - [X] Programming Assignment: Anomaly Detection and Recommender Systems ### Week 10 - [X] Videos: Large Scale Machine Learning - [X] Quiz: Large Scale Machine Learning ### Week 11 - [X] Videos: Application Example: Photo OCR - [X] Quiz: Application: Photo OCR ## Certificate * [Verified Certificate]() ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)

Vectorized Machine Learning in Python 🐍 From Scratch

A MATLAB implementation of the ID3 decision tree algorithm for EECS349 - Machine Learning

MATLAB assignments in Coursera's Machine Learning course