In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Este curso forma parte de Programa Especializado - Recommender Systems
Matrix Factorization and Advanced Techniques
ofrecido por
Programa - Qué aprenderás en este curso
Semana
1Preface
1 video (Total 4 min)
Semana
2Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
5 videos (Total 70 min), 1 reading
5 videos
Singular Value Decomposition17m
Gradient Descent Techniques17m
Deriving FunkSVD11m
Probabilistic Matrix Factorization10m
1 lecturas
On Folding-In with Gradient Descent10m
Semana
3Matrix Factorization (Part 2)
2 videos (Total 15 min), 2 readings, 6 quizzes
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
4Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
6 videos (Total 96 min)
6 videos
Hybrids with Robin Burke16m
Hybridization through Matrix Factorization15m
Matrix Factorization Hybrids with George Karypis17m
Interview with Arindam Banerjee15m
Interview with Yehuda Koren22m
Principales revisiones
por SK•Dec 5th 2017
Awesome course especially for those doing Ph.D in recommender systems
Instructores
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Acerca de University of 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 Recommender Systems
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.
Preguntas Frecuentes
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