Matrix Factorization and Advanced Techniques

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.

University of Minnesota
Created by:   University of Minnesota
Michael D. Ekstrand
Taught by:    Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
Joseph A Konstan
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.
How To PassPass all graded assignments to complete the course.

How It Works
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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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.
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