About this Course

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Learner Career Outcomes

60%

started a new career after completing these courses

36%

got a tangible career benefit from this course

11%

got a pay increase or promotion
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.
Intermediate Level
Approx. 23 hours to complete
English

Skills you will gain

Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems

Learner Career Outcomes

60%

started a new career after completing these courses

36%

got a tangible career benefit from this course

11%

got a pay increase or promotion
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.
Intermediate Level
Approx. 23 hours to complete
English

Offered by

Placeholder

University of Minnesota

Syllabus - What you will learn from this course

Content RatingThumbs Up90%(2,005 ratings)Info
Week
1

Week 1

1 hour to complete

Preface

1 hour to complete
2 videos (Total 41 min), 1 reading
2 videos
Intro to Course and Specialization13m
1 reading
Notes on Course Design and Relationship to Prior Courses10m
4 hours to complete

Introducing Recommender Systems

4 hours to complete
9 videos (Total 147 min), 2 readings, 2 quizzes
9 videos
Preferences and Ratings17m
Predictions and Recommendations16m
Taxonomy of Recommenders I27m
Taxonomy of Recommenders II21m
Tour of Amazon.com21m
Recommender Systems: Past, Present and Future16m
Introducing the Honors Track7m
Honors: Setting up the development environment10m
2 readings
About the Honors Track10m
Downloads and Resources10m
2 practice exercises
Closing Quiz: Introducing Recommender Systems20m
Honors Track Pre-Quiz30m
Week
2

Week 2

10 hours to complete

Non-Personalized and Stereotype-Based Recommenders

10 hours to complete
7 videos (Total 111 min), 5 readings, 9 quizzes
7 videos
Summary Statistics I16m
Summary Statistics II22m
Demographics and Related Approaches13m
Product Association Recommenders19m
Assignment #1 Intro Video14m
Assignment Intro: Programming Non-Personalized Recommenders17m
5 readings
External Readings on Ranking and Scoring10m
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10m
Assignment Intro: Programming Non-Personalized Recommenders10m
LensKit Resources10m
Rating Data Information10m
8 practice exercises
Assignment #1: Response #1: Top Movies by Mean Rating30m
Assignment #1: Response #2: Top Movies by Count30m
Assignment #1: Response #3: Top Movies by Percent Liking30m
Assignment #1: Response #4: Association with Toy Story30m
Assignment #1: Response #5: Correlation with Toy Story30m
Assignment #1: Response #6: Male-Female Differences in Average Rating30m
Assignment #1: Response #7: Male-Female differences in Liking30m
Non-Personalized Recommenders20m
Week
3

Week 3

3 hours to complete

Content-Based Filtering -- Part I

3 hours to complete
8 videos (Total 156 min)
8 videos
TFIDF and Content Filtering24m
Content-Based Filtering: Deeper Dive26m
Entree Style Recommenders -- Robin Burke Interview13m
Case-Based Reasoning -- Interview with Barry Smyth13m
Dialog-Based Recommenders -- Interview with Pearl Pu21m
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11m
Beyond TFIDF -- Interview with Pasquale Lops21m
Week
4

Week 4

6 hours to complete

Content-Based Filtering -- Part II

6 hours to complete
2 videos (Total 26 min), 3 readings, 3 quizzes
2 videos
Honors: Intro to programming assignment10m
3 readings
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1h 20m
Tools for Content-Based Filtering10m
CBF Programming Intro10m
2 practice exercises
Assignment #2 Answer Form20m
Content-Based Filtering20m
1 hour to complete

Course Wrap-up

1 hour to complete
2 videos (Total 45 min), 1 reading
2 videos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1 reading
Related Readings10m

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About the Recommender Systems Specialization

Recommender Systems

Frequently Asked Questions

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