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Matrix Factorization and Advanced Techniques
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.
Duration
3 Months
Institution
University of Minnesota
Format
Online
Eligibility Criteria
school
Academic Foundation
A recognized Bachelor’s degree or high school equivalent required for admission into University of Minnesota.
language
Language Proficiency
English proficiency required. IELTS, TOEFL, or standard medium-of-instruction certificates accepted.
Detailed Fees Breakdown
Base Tuition Fee
$164
Total Est. Investment
$164
Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.
Academic Trajectory
Program Outcome
Graduates of the Matrix Factorization and Advanced Techniques program at University of Minnesota are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.