Big Data Analytics Big Data Analytics D. Distributed Machine Learning Algorithms / D.2 Distributed Matrix Factorization Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 24 Big Data Analytics Syllabus Tue. 4.4. (1) 0. Introduction Tue. 11.4. Tue. 18.4. (2) (3) A. Parallel Computing A.1 Threads A.2 Message Passing Interface (MPI) Tue. 25.4. Tue. 2.5. Tue. 9.5. (4) (5) (6) B. Distributed Storage B.1 Distributed File Systems B.2 Partioning of Relational Databases B.3 NoSQL Databases Tue. 16.5. (7) A.3 Graphical Processing Units (GPUs) Tue. 23.5. Tue. 30.5. Tue. 6.6. (8) (9) C. Distributed Computing Environments C.1 Map-Reduce C.2 Resilient Distributed Datasets (Spark) Pentecoste Break — Tue. 13.6. Tue. 20.6. Tue. 27.6. Tue. 4.7. (10) (11) (12) (13) D. Distributed Machine Learning Algorithms D.1 Distributed Stochastic Gradient Descent D.2 Distributed Matrix Factorization D.3 Alternating Direction Method of Multipliers (ADMM) Questions and Answers Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 24 Big Data Analytics Outline 1. Introduction 2. Matrix Factorization via Distributed SGD 3. NOMAD Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 24 Big Data Analytics 1. Introduction Outline 1. Introduction 2. Matrix Factorization via Distributed SGD 3. NOMAD Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 24 Big Data Analytics 1. Introduction The Matrix Completion Problem Given I the values D ⊆ [N] × [M] × R of some cells of an unknown matrix Y ∈ RN×M (called data) and I a function ` : R × R → R (called loss), predict the values of the missing