About this Course
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Flexible deadlines

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Approx. 8 hours to complete

Suggested: 10 hours/week...

English

Subtitles: English

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...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 minutes to complete

Preface

...
1 video (Total 4 min)
Week
2
1 hour to complete

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.

...
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
Week
3
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
Week
4
2 hours to complete

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.

...
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
Week
5
23 minutes to complete

Advanced Machine Learning

...
3 videos (Total 23 min)
3 videos
Learning to Rank: Interview with Xavier Amatriain21s
Personalized Ranking (with Daniel Kluver)11m
Week
6
6 hours to complete

Advanced Topics

...
7 videos (Total 133 min), 1 reading, 3 quizzes
7 videos
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)22m
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)18m
Industry Practical Issues: Inteview with Anmol Bhasin26m
Recommending Music - Interview with Paul Lamere13m
Specialization Wrap Up21m
Programming Hybrids & Learning to Rank9m
1 reading
Programming Hybrids and Machine Learning Description10m
2 practice exercises
Hybrid and Advanced Techniques Quiz12m
Honors Hybrid Assignment Evaluation Quiz10m
4.2
18 ReviewsChevron Right

50%

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

Instructors

Avatar

Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University
Avatar

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

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A 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.