Graph Algorithms and Machine Learning
offered by MIT University

Private University
168 acre campus
Estd. 1861

Graph Algorithms and Machine Learning
at
MIT University
Overview

Acquire a new set of tools for improving the effectiveness and performance of machine learning pipelines

Duration	2 days
Total fee	₹1.95 Lakh
Mode of learning	Online
Schedule type	Self paced
Course Level	UG Certificate

Graph Algorithms and Machine Learning
at
MIT University
Course details

Who should do this course?

For Data scientists
For Software engineers looking to develop fast graph software
For Project managers

What are the course deliverables?

Learn how to model structured data with graphs
Enhance your understanding of real-world graph properties and how to generate synthetic graphs
Master fundamental graph algorithms
Discuss parallelism and how it can be used to speed up graph processing
Examine performance characteristics of graph algorithms
Assess the state-of-the-art graph processing tools available today and learn to use certain graph software
Explore the pros and cons of different graph processing approaches

More about this course

This accelerated course provides a comprehensive overview of critical topics in graph analytics, including applications of graphs, the structure of real-world graphs, fast graph algorithms, synthetic graph generation, performance optimizations, programming frameworks, and learning on graphs
The curriculum additionally covers software performance engineering concepts, such as parallelism, caching, and compression, in the context of graph processing, as well as different design choices that will enable you to use or design the appropriate graph solutions for your needs
Through tutorials, exercises, and demonstrations featuring state-of-the-art graph analytics tools, you will broaden your fundamental understanding of graph analytics, and master the techniques and tools that you need to efficiently solve large-scale graph problems in your organization

Graph Algorithms and Machine Learning
at
MIT University
Curriculum

Day 1

Introduction to Graph Theory and Applications of Graphs

Structure of Real-World Graphs (Part 1)

Structure of Real-World Graphs (Part 2)

Graph Algorithms (Part 1)

Graph Algorithms (Part 2)

Day 2

Demo and Exercises with Graph Processing Software (NetworkX)

Large-Scale Graph Processing Frameworks

Machine Learning on Graphs

Problem Clinic

Graph Algorithms and Machine Learning
at
MIT University
Faculty details

Julian Shun

Julian Shun is an Associate Professor of Electrical Engineering and Computer Science at MIT and a lead investigator in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).