About this Course

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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 8 hours to complete

Suggested: 1-3 weeks of study, 3-5 hours per 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: 1-3 weeks of study, 3-5 hours per week...

English

Subtitles: English

Instructors

Image of instructor, Michael D. Ekstrand

Michael D. Ekstrand 

Assistant Professor
Dept. of Computer Science, Boise State University
87,756 Learners
6 Courses
Image of instructor, Joseph A Konstan

Joseph A Konstan 

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
124,126 Learners
11 Courses

Offered by

University of Minnesota logo

University of Minnesota

Syllabus - What you will learn from this course

Week
1

Week 1

4 hours to complete

Capstone Project

4 hours to complete
2 videos (Total 6 min), 2 readings, 3 quizzes
2 videos
Capstone Wrap-Up1m
2 readings
Capstone Assignment (all versions combined)10m
Thank you!10m
1 practice exercise
Certification for honors track2m

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.