University of Minnesota, Twin Cities - Matrix Factorization and Advanced Techniques
- Offered byCoursera
- Public/Government Institute
Matrix Factorization and Advanced Techniques at Coursera Overview
Duration | 16 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Matrix Factorization and Advanced Techniques at Coursera Highlights
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Matrix Factorization and Advanced Techniques at Coursera Course details
- In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Matrix Factorization and Advanced Techniques at Coursera Curriculum
Preface
Matrix Factorization and Advanced Techniques
Matrix Factorization (Part 1)
Introduction to Matrix Factorization and Dimensionality Reduction
Singular Value Decomposition
Gradient Descent Techniques
Deriving FunkSVD
Probabilistic Matrix Factorization
On Folding-In with Gradient Descent
Matrix Factorization (Part 2)
Assignment Introduction
Programming Matrix Factorization
Assignment Instructions
Intro - Programming Matrix Factorization
Matrix Factorization Assignment Part l
Matrix Factorization Assignment Part ll
Matrix Factorization Assignment Part lll
Matrix Factorization Quiz
SVD Programming Eval Quiz
Hybrid Recommenders
Hybrid Recommenders
Hybrids with Robin Burke
Hybridization through Matrix Factorization
Matrix Factorization Hybrids with George Karypis
Interview with Arindam Banerjee
Interview with Yehuda Koren
Advanced Machine Learning
Learning Recommenders
Learning to Rank: Interview with Xavier Amatriain
Personalized Ranking (with Daniel Kluver)
Advanced Topics
Context-Aware Recommendation I : Interview with Francesco Ricci
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
Industry Practical Issues: Inteview with Anmol Bhasin
Recommending Music - Interview with Paul Lamere
Specialization Wrap Up
Programming Hybrids & Learning to Rank
Programming Hybrids and Machine Learning Description
Hybrid and Advanced Techniques Quiz
Honors Hybrid Assignment Evaluation Quiz
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