🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained
-
Updated
Jul 8, 2020 - MATLAB
🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20
机器学习-Coursera-吴恩达- python+Matlab代码实现
A MATLAB toolbox for classifier: Version 1.0.7
Matlab library for gradient descent algorithms: Version 1.0.1
My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.
A matlab EEG toolbox to perform overlap correction and non-linear & linear regression.
Andrew Ng's Machine Learning Course
💡This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave.
Variational Bayes linear and logistic regression
MATLAB implementation of Gradient Descent algorithm for Multivariate Linear Regression
Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DM…
In this work, we use convex optimization package in MATLAB to implement multi-user transmit beamforming problem and linear regression. This is the homework 2 of ELEC 5470 Convex Optimization, HKUST.
# 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…
Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.
Project on blood pressure estimation from ECG and PPG signals.
Bayesian zero-constrained regression with compositional covariates
Several ML Algorithms implemented from scratch, without using inbuilt libraries. Regression Models, GDA, SVM, Naive Bayes, Decision Tree, PCA using SVD, Neural Network
Stanford's Machine Learning Exercises
Add a description, image, and links to the linear-regression topic page so that developers can more easily learn about it.
To associate your repository with the linear-regression topic, visit your repo's landing page and select "manage topics."