KDD17_FMG

Probabilistic Matrix Factorization with Social Trust for Recommendation (Ma et al. SIGIR 2009)

Andrew Ng's Machine Learning Course

A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm.

A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks

MATLAB Implementation of the CGPRANK algorithm

Movie Recommendation using Cascading Bandits namely CascadeLinTS and CascadeLinUCB

A Probabilistic Graphical approach to detect different types of shilling attacks on Recommender Systems.

Nonnegative matrix factorization with DAG constraints. A probabilistic formulation, variational learning.

The codes have been provided to support the article "Novel implicit-trust-network-based recommendation methodology", which has been published in Expert Systems With Applications. This algorithmic framework is abbreviated to ITNRM. It first generates implicit trust networks to find users' trustees or neighbors. And a novel recommendation methodol…

Machine Learning from Stanford University (Andrew Ng) - Assignments and Lectures

This repository shows code of programming tasks which I completed during Machine Learning course on Coursera.

📊 📈 In depth explained my assignment solutions. Grade: 97.3%

works for ML course by Andrew Ng

These are the solutions to the programming assigments from Andrew Ng's "Machine Learning" course from Coursera

Scripts for machine learning algorithms in MATLAB/Octave and python

Implement the anomaly detection algorithm which is widely used in fraud detection (e.g. ‘has this credit card been stolen?’) and apply it to detect failing servers on a network. And use collaborative filtering to build a recommender system for movies, which are used by companies like Amazon, Netflix, and Apple to recommend products to their user…

My Machine Learning Course from Stanford University using #Octave and #MATLAB to solve ML algorithms and problems

This Repository contains Solutions to Lab Assignments/slides and my personal Notes of the Machine Learning (2022) from Stanford University on Coursera taught by Andrew Ng.