Acerca de este curso: 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.
Creada por: Universidad de Minnesota
Enseñado por: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
Enseñado por: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
| Información básica | Curso 4 de 5 en Recommender Systems Specialization |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
Preface
SEMANA 2
Matrix 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, 1 reading
- Vídeo: Introduction to Matrix Factorization and Dimensionality Reduction
- Vídeo: Singular Value Decomposition
- Vídeo: Gradient Descent Techniques
- Reading: On Folding-In with Gradient Descent
- Vídeo: Deriving FunkSVD
- Vídeo: Probabilistic Matrix Factorization
SEMANA 3
Matrix Factorization (Part 2)
2 videos, 2 readings
- Vídeo: Assignment Introduction
- Reading: Assignment Instructions
- Reading: Intro - Programming Matrix Factorization
- Vídeo: Programming Matrix Factorization
Calificado: Matrix Factorization Assignment Part l
Calificado: Matrix Factorization Assignment Part ll
Calificado: Matrix Factorization Assignment Part lll
Calificado: Matrix Factorization Quiz
Calificado: Programming SVD
Calificado: SVD Programming Eval Quiz
SEMANA 4
Hybrid 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
- Vídeo: Hybrid Recommenders
- Vídeo: Hybrids with Robin Burke
- Vídeo: Hybridization through Matrix Factorization
- Vídeo: Matrix Factorization Hybrids with George Karypis
- Vídeo: Interview with Arindam Banerjee
- Vídeo: Interview with Yehuda Koren
SEMANA 5
Advanced Machine Learning
3 videos
- Vídeo: Learning Recommenders
- Vídeo: Learning to Rank: Interview with Xavier Amatriain
- Vídeo: Personalized Ranking (with Daniel Kluver)
SEMANA 6
Advanced Topics
7 videos, 1 reading
- Vídeo: Context-Aware Recommendation I : Interview with Francesco Ricci
- Vídeo: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
- Vídeo: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
- Vídeo: Industry Practical Issues: Inteview with Anmol Bhasin
- Vídeo: Recommending Music - Interview with Paul Lamere
- Vídeo: Specialization Wrap Up
- Reading: Programming Hybrids and Machine Learning Description
- Vídeo: Programming Hybrids & Learning to Rank
Calificado: Hybrid and Advanced Techniques Quiz
Calificado: Programming Hybrids and Learning-to-Rank
Calificado: Honors Hybrid Assignment Evaluation Quiz
Preguntas Frecuentes
Cómo funciona
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
The Hybrid recommenders i.e. Week 4 needs more explanation especially what is Tensor factorization etc, Week 4 was difficult to grasp. Week 5 and Week 6 was informative especially the LinkedIn video and Learning to Rank: Interview with Xavier Amatriain in which a problem was discussed of having that popularity vs Ranking issue.
Thanks it was quite a lot new things to learn ! My Rating 4.5
Great course. Professors do an excellent job of breaking down this stuff into digestible bits without losing much substance. Highly recommend. You'll need to do some outside exploration and learning though.
Awesome course especially for those doing Ph.D in recommender systems
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KS
Based on my experience with the previous courses in this specialization, I was very positively surprised by the amount and depth of material provided in this course. It covers almost everything that is there to be known. The comprehensive interviews are a big plus point. Also, this course provides guidelines as to how to develop, employ and evaluate a recommender system in real life. I would definitely recommend anyone interested in the field to take this course.