Acerca de este Curso
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Aprox. 8 horas para completar

Sugerido: 10 hours/week...

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100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Aprox. 8 horas para completar

Sugerido: 10 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
4 minutos para completar

Preface

1 video (Total 4 minutos)
Semana
2
1 hora para completar

Matrix Factorization (Part 1)

5 videos (Total 70 minutos), 1 reading
5 videos
Singular Value Decomposition17m
Gradient Descent Techniques17m
Deriving FunkSVD11m
Probabilistic Matrix Factorization10m
1 lectura
On Folding-In with Gradient Descent10m
Semana
3
4 horas para completar

Matrix Factorization (Part 2)

2 videos (Total 15 minutos), 2 readings, 6 quizzes
2 videos
Programming Matrix Factorization6m
2 lecturas
Assignment Instructions10m
Intro - Programming Matrix Factorization10m
5 ejercicios de práctica
Matrix Factorization Assignment Part l10m
Matrix Factorization Assignment Part ll10m
Matrix Factorization Assignment Part lll10m
Matrix Factorization Quiz8m
SVD Programming Eval Quiz6m
Semana
4
2 horas para completar

Hybrid Recommenders

6 videos (Total 96 minutos)
6 videos
Hybrids with Robin Burke16m
Hybridization through Matrix Factorization15m
Matrix Factorization Hybrids with George Karypis17m
Interview with Arindam Banerjee15m
Interview with Yehuda Koren22m
4.3
18 revisionesChevron Right

50%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones sobre Matrix Factorization and Advanced Techniques

por LLJul 19th 2017

great courses! They invite a lot of interviews to let me understand the sea of recommend system!

por SKDec 5th 2017

Awesome course especially for those doing Ph.D in recommender systems

Instructores

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Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University
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Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer 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 del 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....
Sistemas de recomendación

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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