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Recommender Systems Specialization 

Recommender Systems Specialization
 at 
UMN 
Overview

Learn to design, build, and evaluate recommender systems for commerce and content

Duration

2 months

Mode of learning

Online

Difficulty level

Intermediate

Official Website

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Course Level

UG Certificate

Recommender Systems Specialization
 at 
UMN 
Highlights

  • Earn a certificate after completion of the course
  • Financial aid available
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Recommender Systems Specialization
 at 
UMN 
Course details

Who should do this course?
  • Data Scientists and Machine Learning Engineers
  • Software Engineers
  • Product Managers
What are the course deliverables?
  • Build recommendation systems
  • Implement collaborative filtering
  • Master spreadsheet based tools
  • Use project-association recommenders
More about this course
  • This course is designed to provide participants with the knowledge and skills necessary to design, develop, and evaluate recommender systems effectively
  • In this course participants will gain insights into different types of recommender systems, collaborative filtering techniques, content-based filtering methods, and evaluation metrics

Recommender Systems Specialization
 at 
UMN 
Curriculum

Introduction to Recommender Systems: Non-Personalized and Content-Based

Nearest Neighbor Collaborative Filtering

Recommender Systems: Evaluation and Metrics

Matrix Factorization and Advanced Techniques

Recommender Systems Capstone

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Recommender Systems Specialization
 at 
UMN 
Faculty details

Joseph A Konstan
Joseph A. Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor of Computer Science and Engineering at the University of Minnesota. His research addresses a variety of human-computer interaction issues, including recommender systems, social computing, and applications of computing to public health
Michael D. Ekstrand
Michael D. Ekstrand is an assistant professor in the Department of Computer Science at Boise State University. His research focuses on how to evaluate and understand recommender systems in terms of user goals and information needs, as well as how to support reproducible recommender systems research. He is the lead developer of LensKit, an open-source toolkit for building, researching, and studying recommender systems.

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Recommender Systems Specialization
 at 
UMN 
Contact Information

Address

301 19th Ave. South
Minneapolis, Minnesota 55455

Minneapolis ( Minnesota)

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