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Matrix Methods
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
Duration
5 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
$118
Total Est. Investment
$118
Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.
Academic Trajectory
Program Outcome
Graduates of the Matrix Methods program at University of Minnesota are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.