About this Course
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Flexible deadlines

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Approx. 8 hours to complete

Suggested: 10 hours/week...


Subtitles: English
Learners taking this Course are
  • Data Scientists
  • Machine Learning Engineers
  • Researchers
  • Research Assistants
  • Data Analysts

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 8 hours to complete

Suggested: 10 hours/week...


Subtitles: English

Syllabus - What you will learn from this course

4 minutes to complete


1 video (Total 4 min)
1 hour to complete

Matrix Factorization (Part 1)

5 videos (Total 70 min), 1 reading
5 videos
Singular Value Decomposition17m
Gradient Descent Techniques17m
Deriving FunkSVD11m
Probabilistic Matrix Factorization10m
1 reading
On Folding-In with Gradient Descent10m
4 hours to complete

Matrix Factorization (Part 2)

2 videos (Total 15 min), 2 readings, 6 quizzes
2 videos
Programming Matrix Factorization6m
2 readings
Assignment Instructions10m
Intro - Programming Matrix Factorization10m
5 practice exercises
Matrix Factorization Assignment Part l10m
Matrix Factorization Assignment Part ll10m
Matrix Factorization Assignment Part lll10m
Matrix Factorization Quiz8m
SVD Programming Eval Quiz6m
2 hours to complete

Hybrid Recommenders

6 videos (Total 96 min)
6 videos
Hybrids with Robin Burke16m
Hybridization through Matrix Factorization15m
Matrix Factorization Hybrids with George Karypis17m
Interview with Arindam Banerjee15m
Interview with Yehuda Koren22m
20 ReviewsChevron Right


got a tangible career benefit from this course

Top reviews from Matrix Factorization and Advanced Techniques

By LLJul 19th 2017

great courses! They invite a lot of interviews to let me understand the sea of recommend system!

By SKDec 5th 2017

Awesome course especially for those doing Ph.D in recommender systems



Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering

About University of Minnesota

The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations....

About the Recommender Systems Specialization

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project....
Recommender Systems

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.