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: Миннесотский университет

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	Course 4 of 5 in the Recommender Systems Specialization
Язык	English
Как пройти курс	Чтобы пройти курс, выполните все оцениваемые задания.
Оценки пользователей	4.3 звезды Средняя оценка пользователей: 4.3Посмотрите, что пишут учащиеся

Программа курса

НЕДЕЛЯ 1

Preface

1 видео

Видео: Matrix Factorization and Advanced Techniques

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: Introduction to Matrix Factorization and Dimensionality Reduction
Видео: Singular Value Decomposition
Видео: Gradient Descent Techniques
Leyendo: On Folding-In with Gradient Descent
Видео: Deriving FunkSVD
Видео: Probabilistic Matrix Factorization

НЕДЕЛЯ 3

Matrix Factorization (Part 2)

2 видео, 2 материалов для самостоятельного изучения

Видео: Assignment Introduction
Leyendo: Assignment Instructions
Leyendo: 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: Hybrid Recommenders
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 Recommenders
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 I : Interview with Francesco Ricci
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
Leyendo: 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

When will I have access to the lectures and assignments?

What will I get if I pay for this course?

What is the refund policy?

Is financial aid available?

Как это работает

Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.

Certificados

Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

Авторы

Миннесотский университет

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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Earn a shareable Course Certificate	Доступен

Рейтинги и отзывы

Оценка 4.3 из 5 по 75 отзывам

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.