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.
本课程是 Recommender Systems 专项课程 专项课程的一部分
Matrix Factorization and Advanced Techniques
提供方
教学大纲 - 您将从这门课程中学到什么
周
1周
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 个视频（共 70 分钟）, 1 个阅读材料
5 个视频
Singular Value Decomposition17分钟
Gradient Descent Techniques17分钟
Deriving FunkSVD11分钟
Probabilistic Matrix Factorization10分钟
1 个阅读材料
On Folding-In with Gradient Descent10分钟
周
3Matrix Factorization (Part 2)
2 个视频（共 15 分钟）, 2 个阅读材料, 6 个测验
2 个阅读材料
Assignment Instructions10分钟
Intro - Programming Matrix Factorization10分钟
5 个练习
Matrix Factorization Assignment Part l10分钟
Matrix Factorization Assignment Part ll10分钟
Matrix Factorization Assignment Part lll10分钟
Matrix Factorization Quiz8分钟
SVD Programming Eval Quiz6分钟
周
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 个视频（共 96 分钟）
6 个视频
Hybrids with Robin Burke16分钟
Hybridization through Matrix Factorization15分钟
Matrix Factorization Hybrids with George Karypis17分钟
Interview with Arindam Banerjee15分钟
Interview with Yehuda Koren22分钟
4.3
16 个审阅热门审阅
创建者 SK•Dec 5th 2017
Awesome course especially for those doing Ph.D in recommender systems
讲师
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
关于 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.
关于 Recommender Systems 专项课程
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.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
