Recommender Systems Specialization offered by University of Minnesota, Twin Cities
- Public University
1 Campus- Estd. 1851
Recommender Systems Specialization at University of Minnesota, Twin Cities Overview
Recommender Systems Specialization
at University of Minnesota, Twin Cities
Learn to design, build, and evaluate recommender systems for commerce and content
Duration | 2 months |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Course Level | UG Certificate |
Recommender Systems Specialization
Table of content
- Overview
- Highlights
- Course Details
- Curriculum
- Faculty
Recommender Systems Specialization at University of Minnesota, Twin Cities Highlights
Recommender Systems Specialization
at University of Minnesota, Twin Cities
- Earn a certificate after completion of the course
- Financial aid available
Recommender Systems Specialization at University of Minnesota, Twin Cities Course details
Recommender Systems Specialization
at University of Minnesota, Twin Cities
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 University of Minnesota, Twin Cities Curriculum
Recommender Systems Specialization
at University of Minnesota, Twin Cities
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
Recommender Systems Specialization at University of Minnesota, Twin Cities Faculty details
Recommender Systems Specialization
at University of Minnesota, Twin Cities
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 University of Minnesota, Twin Cities Contact Information
Recommender Systems Specialization
at University of Minnesota, Twin Cities
Address
301 19th Ave. South
Minneapolis, Minnesota 55455
Minneapolis ( Minnesota)
Phone
Email
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