Big Data Analytics: Distributed Machine Learning Algorithms & Distributed Matrix Factorization

Big Data Analytics: Distributed Machine Learning Algorithms & Distributed Matrix Factorization

Course Overview
This study material focuses on distributed machine learning algorithms with a particular emphasis on distributed matrix factorization techniques. It covers the matrix completion problem, the matrix factorization model, and how stochastic gradient descent (SGD) is used for matrix factorization. Additionally, explore the NOMAD algorithm for parameter updates and its comparison with baseline methods.

Key Topics Covered:

• Introduction:

  • The Matrix Completion Problem: Understand the matrix completion problem and its significance in machine learning and data analysis.
  • The Matrix Factorization Model: Explore the matrix factorization model, a key technique for solving matrix completion problems.
  • Problem Equivalence: Learn about the equivalence of different problem formulations in matrix factorization.
  • Stochastic Gradient Descent (SGD) for Matrix Factorization: Introduction to SGD and how it is applied to matrix factorization for efficient optimization.

• Matrix Factorization via Distributed SGD:

  • Idea: Understand the core idea behind distributed SGD for matrix factorization.
  • Distributed SGD for Matrix Factorization: Explore the implementation of distributed SGD in matrix factorization, including techniques for parallelizing the computation.
  • Experiments / Results: Review experiments and results demonstrating the effectiveness of distributed SGD for matrix factorization.

• NOMAD:

  • Parameter Updates: Learn about parameter updates in the NOMAD algorithm and their role in distributed machine learning.
  • Parameter Partitioning: Explore how parameters are partitioned in NOMAD to facilitate distributed computation.
  • Algorithm: Detailed explanation of the NOMAD algorithm, including its approach to distributed matrix factorization.
  • Results: Analyze results from applying NOMAD, including performance metrics and outcomes.
  • Comparison with Baselines: Compare NOMAD with baseline methods to understand its advantages and improvements.

Why Choose This Material?

• In-depth coverage of distributed matrix factorization and machine learning algorithms.
• Practical examples and results from experiments to illustrate key concepts.
• Ideal for students, data scientists, and machine learning practitioners interested in advanced distributed learning techniques.

This material is perfect for individuals looking to gain a deep understanding of distributed matrix factorization and how it integrates with machine learning algorithms.