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Matrix Factorization and Advanced Techniques (Coursera)

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.

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)

Graded: Matrix Factorization Assignment Part l

Graded: Matrix Factorization Assignment Part ll

Graded: Matrix Factorization Assignment Part lll

Graded: Matrix Factorization Quiz

Graded: Programming SVD

Graded: SVD Programming Eval Quiz

WEEK 4

Hybrid Recommenders

This is a three-part, two-week module on hybrid and machine learning recommendaton 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

Graded: Hybrid and Advanced Techniques Quiz

Graded: Programming Hybrids and Learning-to-Rank

Graded: Honors Hybrid Assignment Evaluation Quiz

ENROLL IN COURSE