Об этом курсе: 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.
Автор: University of Minnesota
Преподаватели: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
Преподаватели: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
| Основные сведения | Курс 4 из 5 — Recommender Systems Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Preface
1 видео
НЕДЕЛЯ 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 видео, 1 материал для самостоятельного изучения
- Видео: Singular Value Decomposition
- Видео: Gradient Descent Techniques
- Reading: On Folding-In with Gradient Descent
- Видео: Deriving FunkSVD
- Видео: Probabilistic Matrix Factorization
НЕДЕЛЯ 3
Matrix Factorization (Part 2)
2 видео, 2 материала для самостоятельного изучения
- Reading: Assignment Instructions
- Reading: Intro - Programming Matrix Factorization
- Видео: Programming Matrix Factorization
Оцениваемый: Matrix Factorization Assignment Part l
Оцениваемый: Matrix Factorization Assignment Part ll
Оцениваемый: Matrix Factorization Assignment Part lll
Оцениваемый: Matrix Factorization Quiz
Оцениваемый: Programming SVD
Оцениваемый: SVD Programming Eval Quiz
НЕДЕЛЯ 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 видео
- Видео: Hybrids with Robin Burke
- Видео: Hybridization through Matrix Factorization
- Видео: Matrix Factorization Hybrids with George Karypis
- Видео: Interview with Arindam Banerjee
- Видео: Interview with Yehuda Koren
НЕДЕЛЯ 5
Advanced Machine Learning
3 видео
- Видео: Learning to Rank: Interview with Xavier Amatriain
- Видео: Personalized Ranking (with Daniel Kluver)
НЕДЕЛЯ 6
Advanced Topics
7 видео, 1 материал для самостоятельного изучения
- Видео: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
- Видео: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
- Видео: Industry Practical Issues: Inteview with Anmol Bhasin
- Видео: Recommending Music - Interview with Paul Lamere
- Видео: Specialization Wrap Up
- Reading: Programming Hybrids and Machine Learning Description
- Видео: Programming Hybrids & Learning to Rank
Оцениваемый: Hybrid and Advanced Techniques Quiz
Оцениваемый: Programming Hybrids and Learning-to-Rank
Оцениваемый: Honors Hybrid Assignment Evaluation Quiz
Часто задаваемые вопросы
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Авторы
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.
Стоимость
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Рейтинги и отзывы
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.
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.
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Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.