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.
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강의 계획 - 이 강좌에서 배울 내용
주
1주
2완료하는 데 1시간 필요
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 (Total 70 min), 1 reading
5개의 동영상
Singular Value Decomposition17m
Gradient Descent Techniques17m
Deriving FunkSVD11m
Probabilistic Matrix Factorization10m
1개의 읽기 자료
On Folding-In with Gradient Descent10m
주
3완료하는 데 4시간 필요
Matrix Factorization (Part 2)
2 videos (Total 15 min), 2 readings, 6 quizzes
2개의 읽기 자료
Assignment Instructions10m
Intro - Programming Matrix Factorization10m
5개 연습문제
Matrix Factorization Assignment Part l10m
Matrix Factorization Assignment Part ll10m
Matrix Factorization Assignment Part lll10m
Matrix Factorization Quiz8m
SVD Programming Eval Quiz6m
주
4완료하는 데 2시간 필요
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 (Total 96 min)
6개의 동영상
Hybrids with Robin Burke16m
Hybridization through Matrix Factorization15m
Matrix Factorization Hybrids with George Karypis17m
Interview with Arindam Banerjee15m
Interview with Yehuda Koren22m
주
5완료하는 데 23분 필요
Advanced Machine Learning
3 videos (Total 23 min)
3개의 동영상
Learning to Rank: Interview with Xavier Amatriain21
Personalized Ranking (with Daniel Kluver)11m
주
6완료하는 데 6시간 필요
Advanced Topics
7 videos (Total 133 min), 1 reading, 3 quizzes
7개의 동영상
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)22m
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)18m
Industry Practical Issues: Inteview with Anmol Bhasin26m
Recommending Music - Interview with Paul Lamere13m
Specialization Wrap Up21m
Programming Hybrids & Learning to Rank9m
1개의 읽기 자료
Programming Hybrids and Machine Learning Description10m
2개 연습문제
Hybrid and Advanced Techniques Quiz12m
Honors Hybrid Assignment Evaluation Quiz10m
4.2
18개의 리뷰50%
이 강좌를 통해 확실한 경력상 이점 얻기
최상위 리뷰
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
Awesome course especially for those doing Ph.D in recommender systems
강사
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
미네소타 대학교 정보
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.
추천 시스템 전문 분야 정보
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.
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