課程信息
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第 4 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

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完成時間大約為8 小時

建議:10 hours/week...

英語(English)

字幕:英語(English)
學習Course的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Researchers
  • Research Assistants
  • Data Analysts

第 4 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為8 小時

建議:10 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 4 分鐘

Preface

1 個視頻 (總計 4 分鐘)
2
完成時間為 1 小時

Matrix Factorization (Part 1)

5 個視頻 (總計 70 分鐘), 1 個閱讀材料
5 個視頻
Singular Value Decomposition17分鐘
Gradient Descent Techniques17分鐘
Deriving FunkSVD11分鐘
Probabilistic Matrix Factorization10分鐘
1 個閱讀材料
On Folding-In with Gradient Descent10分鐘
3
完成時間為 4 小時

Matrix Factorization (Part 2)

2 個視頻 (總計 15 分鐘), 2 個閱讀材料, 6 個測驗
2 個視頻
Programming Matrix Factorization6分鐘
2 個閱讀材料
Assignment Instructions10分鐘
Intro - Programming Matrix Factorization10分鐘
5 個練習
Matrix Factorization Assignment Part l10分鐘
Matrix Factorization Assignment Part ll10分鐘
Matrix Factorization Assignment Part lll10分鐘
Matrix Factorization Quiz8分鐘
SVD Programming Eval Quiz6分鐘
4
完成時間為 2 小時

Hybrid Recommenders

6 個視頻 (總計 96 分鐘)
6 個視頻
Hybrids with Robin Burke16分鐘
Hybridization through Matrix Factorization15分鐘
Matrix Factorization Hybrids with George Karypis17分鐘
Interview with Arindam Banerjee15分鐘
Interview with Yehuda Koren22分鐘
4.3
20 個審閱Chevron Right

50%

通過此課程獲得實實在在的工作福利

來自Matrix Factorization and Advanced Techniques的熱門評論

創建者 LLJul 19th 2017

great courses! They invite a lot of interviews to let me understand the sea of recommend system!

創建者 SKDec 5th 2017

Awesome course especially for those doing Ph.D in recommender systems

講師

Avatar

Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University
Avatar

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering

關於 明尼苏达大学

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....

關於 推荐系统 專項課程

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project....
推荐系统

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