Matrix Factorization and Advanced Techniques

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Description

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  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  University of Minnesota
  • Taught by:  Michael D. Ekstrand, Assistant Professor

    Dept. of Computer Science, Boise State University
  • Taught by:  Joseph A Konstan, Distinguished Mc…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  University of Minnesota
  • Taught by:  Michael D. Ekstrand, Assistant Professor

    Dept. of Computer Science, Boise State University
  • Taught by:  Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor

    Computer Science and Engineering
Basic Info Course 4 of 5 in the Recommender Systems Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

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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.

Syllabus


WEEK 1


Preface



1 video expand


  1. Video: Matrix Factorization and Advanced Techniques


WEEK 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 videos, 1 reading expand


  1. Video: Introduction to Matrix Factorization and Dimensionality Reduction
  2. Video: Singular Value Decomposition
  3. Video: Gradient Descent Techniques
  4. Reading: On Folding-In with Gradient Descent
  5. Video: Deriving FunkSVD
  6. Video: Probabilistic Matrix Factorization


WEEK 3


Matrix Factorization (Part 2)



2 videos, 2 readings expand


  1. Video: Assignment Introduction
  2. Reading: Assignment Instructions
  3. Reading: Intro - Programming Matrix Factorization
  4. Video: Programming Matrix Factorization

Graded: Matrix Factorization Assignment Part l
Graded: Matrix Factorization Assignment Part ll
Graded: Matrix Factorization Assignment Part lll
Graded: Matrix Factorization Quiz
Graded: Programming SVD
Graded: SVD Programming Eval Quiz

WEEK 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 videos expand


  1. Video: Hybrid Recommenders
  2. Video: Hybrids with Robin Burke
  3. Video: Hybridization through Matrix Factorization
  4. Video: Matrix Factorization Hybrids with George Karypis
  5. Video: Interview with Arindam Banerjee
  6. Video: Interview with Yehuda Koren


WEEK 5


Advanced Machine Learning



3 videos expand


  1. Video: Learning Recommenders
  2. Video: Learning to Rank: Interview with Xavier Amatriain
  3. Video: Personalized Ranking (with Daniel Kluver)


WEEK 6


Advanced Topics



7 videos, 1 reading expand


  1. Video: Context-Aware Recommendation I : Interview with Francesco Ricci
  2. Video: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
  3. Video: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
  4. Video: Industry Practical Issues: Inteview with Anmol Bhasin
  5. Video: Recommending Music - Interview with Paul Lamere
  6. Video: Specialization Wrap Up
  7. Reading: Programming Hybrids and Machine Learning Description
  8. Video: Programming Hybrids & Learning to Rank

Graded: Hybrid and Advanced Techniques Quiz
Graded: Programming Hybrids and Learning-to-Rank
Graded: Honors Hybrid Assignment Evaluation Quiz
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There are no frequently asked questions yet. Send an Email to info@springest.com