This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance. Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration. Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution.
Este curso forma parte de Programa Especializado - Sistemas de recomendación
Recommender Systems Capstone
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Acerca de este Curso
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Curso 5 de 5 en
Fechas límite flexibles
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Aprox. 8 horas para completar
Sugerido: 1-3 weeks of study, 3-5 hours per week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
4 horas para completar
Capstone Project
4 horas para completar
2 videos (Total 6 minutos), 2 lecturas, 3 cuestionarios
2 lecturas
Capstone Assignment (all versions combined)10m
Thank you!10m
1 ejercicio de práctica
Certification for honors track2m
Instructores
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Acerca de Universidad de Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Acerca de Programa especializado Sistemas de recomendación
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.
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