Coursera

Matrix Factorization and Advanced Techniques (Coursera)

Offered by University of Minnesota,

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.

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Course 4 of 5 in the Recommender Systems Specialization.

Syllabus

WEEK 1
Preface

WEEK 2
Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.

WEEK 3
Matrix Factorization (Part 2)

WEEK 4
Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendation algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.

WEEK 5
Advanced Machine Learning

WEEK 6
Advanced Topics

Go to Class

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