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Distributed Optimization And Machine Learning

Understanding the fundamentals of distributed optimization and machine learning

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IIT Bombay and NPTEL

About the Course

This 12-week comprehensive course offers an in-depth overview of designing and analysing distributed optimisation algorithms for machine learning applications. Centralised processing over large networks is computationally prohibitive due to communication overhead and scale. Thus, distributed or decentralised control and optimisation algorithms are essential. The course revisits classical algorithms for centralised optimisation and extends them to distributed settings, addressing communication constraints, network topology, computational resources, and robustness. Topics include graph theory, iterative methods for convex problems, synchronous and asynchronous setups, consensus algorithms, and distributed machine learning with recent literature on accelerated distributed optimisation algorithms.

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1.What You Will Learn?
2.Who Should Go For It?
3.Points to Note
4.Summary

The course provides a comprehensive curriculum to dive into various aspects of distributed optimisation and machine learning. Here's a glimpse of the key topics covered:

Introduction to Distributed Optimization
Mathematical Optimization, Convex Sets and Convex Functions
Strong Convexity and Its Implications
Constrained Optimization Problems: Primal and Lagrangian Dual
KKT Conditions and Primal/Dual Methods
Analysis of Gradient Descent
Analysis of Accelerated Optimization Algorithms
Optimization Algorithms as Dynamical Systems and Stability Theory
Lyapunov Analysis of Gradient Flows
Gradient Flows for Equality Constrained Optimization

It is designed for individuals with a foundation in specific areas to ensure they can grasp the concepts effectively and will be an ideal choice for those who wish to understand and apply their knowledge of:

Computer science students
Machine learning enthusiasts
Data science professionals
Software engineers
Researchers in optimization and machine learning
The course is free of charge, making it a fantastic opportunity to gain valuable knowledge in distributed optimisation and machine learning
It positions distributed optimisation and machine learning enthusiasts for upgraded exciting career opportunities.
Provides high-quality video lectures to enhance the learning experience thoroughly
There are four categories of certificates- Gold, Silver, Elite or Completed that a student will receive depending on his/her final score

Summary

Field of Study:Distributed Optimization And Machine Learning
Type:Free Course
Duration:12 Weeks
Mode of Learning:Online
Average Salary Offered:The average salary offered in this field ranges from ₹ 2.6 LPA to ₹ 14 LPA depending upon your skills, experience, and level of knowledge.
Employment Roles:
  • Data Scientist
  • Machine Learning Engineer
  • Optimization Specialist
  • Distributed Systems Engineer
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