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Learner Reviews & Feedback for Matrix Factorization and Advanced Techniques by University of Minnesota
186 ratings
About the Course
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.
Top reviews
SK
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Awesome course especially for those doing Ph.D in recommender systems
DD
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Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.
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26 - 27 of 27 Reviews for Matrix Factorization and Advanced Techniques
By Moustafa M
•Apr 18, 2020
The HWs for the Honor track had mistakes
By PRATIK K C
•Jun 9, 2020
Could have been better