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Matrix Factorization and Advanced Techniques
  • Level Foundation
  • Duration 16 hours
  • Course by University of Minnesota
  • Offered by

About

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.

Modules

Preface

    • 1 Videos
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1 Videos

  • Matrix Factorization and Advanced Techniques

Matrix Factorization (Part 1)

    • 5 Videos
    • 1 Readings
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5 Videos

  • Introduction to Matrix Factorization and Dimensionality Reduction
  • Singular Value Decomposition
  • Gradient Descent Techniques
  • Deriving FunkSVD
  • Probabilistic Matrix Factorization

1 Readings

  • On Folding-In with Gradient Descent

Matrix Factorization (Part 2)

    • 1 Videos
    • 1 Readings
    • 4 Assignment
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4 Assignment

  • Matrix Factorization Assignment Part l
  • Matrix Factorization Assignment Part ll
  • Matrix Factorization Assignment Part lll
  • Matrix Factorization Quiz

1 Videos

  • Assignment Introduction

1 Readings

  • Assignment Instructions

Honors Assignment

    • 1 Videos
    • 1 Readings
    • 1 Programming
    • 1 Assignment
Show info about module content

1 Programming

  • Programming SVD

1 Assignment

  • SVD Programming Eval Quiz

1 Videos

  • Programming Matrix Factorization

1 Readings

  • Intro - Programming Matrix Factorization

Hybrid Recommenders

    • 6 Videos
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6 Videos

  • Hybrid Recommenders
  • Hybrids with Robin Burke
  • Hybridization through Matrix Factorization
  • Matrix Factorization Hybrids with George Karypis
  • Interview with Arindam Banerjee
  • Interview with Yehuda Koren

Advanced Machine Learning

    • 3 Videos
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3 Videos

  • Learning Recommenders
  • Learning to Rank: Interview with Xavier Amatriain
  • Personalized Ranking (with Daniel Kluver)

Advanced Topics

    • 6 Videos
    • 1 Assignment
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1 Assignment

  • Hybrid and Advanced Techniques Quiz

6 Videos

  • Context-Aware Recommendation I : Interview with Francesco Ricci
  • Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
  • Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
  • Industry Practical Issues: Inteview with Anmol Bhasin
  • Recommending Music - Interview with Paul Lamere
  • Specialization Wrap Up

Honors

    • 1 Videos
    • 1 Readings
    • 1 Programming
    • 1 Assignment
Show info about module content

1 Programming

  • Programming Hybrids and Learning-to-Rank

1 Assignment

  • Honors Hybrid Assignment Evaluation Quiz

1 Videos

  • Programming Hybrids & Learning to Rank

1 Readings

  • Programming Hybrids and Machine Learning Description

Auto Summary

Unlock the potential of advanced recommender systems with our foundational course on Matrix Factorization and Advanced Techniques, offered through Coursera. This course delves into the realm of Data Science and AI, focusing on sophisticated approaches to enhance recommendation algorithms. Guided by expert instructors, you'll start with the basics of matrix factorization, gaining a thorough understanding of both the underlying concepts and practical applications. As you progress, you'll explore hybrid machine learning techniques that merge various algorithms' strengths to create powerful, efficient recommender systems. With a comprehensive duration of 960 minutes, this course ensures a deep and detailed learning experience. Flexible subscription options, including Starter and Professional, cater to different learning needs and professional goals. Ideal for learners at the foundational level, this course is perfect for those looking to build or enhance their knowledge in data science and AI, particularly in the context of recommender systems. Join us and take the first step towards mastering advanced recommendation techniques.

Michael D. Ekstrand
Instructors

Michael D. Ekstrand

Joseph A Konstan
Instructors

Joseph A Konstan