About this Course

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Flexible deadlines
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Approx. 7 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 7 hours to complete
English
Subtitles: English

Offered by

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University of Minnesota

Syllabus - What you will learn from this course

Week
1

Week 1

14 minutes to complete

Preface

14 minutes to complete
2 videos (Total 14 min)
2 videos
The Goals of Evaluation10m
2 hours to complete

Basic Prediction and Recommendation Metrics

2 hours to complete
5 videos (Total 57 min), 1 reading, 1 quiz
5 videos
Prediction Accuracy Metrics12m
Decision Support Metrics16m
Rank-Aware Top-N Metrics18m
Assignment Intro Video2m
1 reading
Metric Computation Assignment Instructions10m
1 practice exercise
Basic Prediction and Recommendation Metrics Assignment42m
Week
2

Week 2

2 hours to complete

Advanced Metrics and Offline Evaluation

2 hours to complete
6 videos (Total 76 min), 1 reading, 2 quizzes
6 videos
Additional Item and List-Based Metrics18m
Experimental Protocols13m
Unary Data Evaluation11m
Temporal Evaluation of Recommenders (Interview with Neal Lathia)12m
Programming Assignment Introduction8m
1 reading
Evaluating Recommenders10m
2 practice exercises
Offline Evaluation and Metrics Quiz22m
Programming Assignment Quiz28m
Week
3

Week 3

1 hour to complete

Online Evaluation

1 hour to complete
4 videos (Total 66 min)
4 videos
Usage Logs and Analysis10m
A/B Studies (Field Experiments)11m
User-Centered Evaluation (Interview with Bart Knijnenburg)25m
1 practice exercise
Online Evaluation Quiz8m
Week
4

Week 4

1 hour to complete

Evaluation Design

1 hour to complete
3 videos (Total 31 min), 2 readings, 1 quiz
3 videos
Case Examples17m
Assignment Intro Video2m
2 readings
Intro to Assignment: Evaluation Design Cases10m
Quiz Debrief10m
1 practice exercise
Assignment: Evaluation Design Cases12m

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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

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

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    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

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