Admissions Open for JANUARY Batch
Learn mathematical concepts for graph data and network analysis.
Days : Tue & Thu
Duration : 3 Hours
Timings: 8 - 10 PM IST
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3 Hours
9 - 10 PM IST
Tue & Thu
Maths for
Graph Machine Learning
Learn mathematical concepts for graph data and network analysis.
Online Live Instructor-Led Learning
3 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Graph Laplacians and adjacency matrices
Message passing mechanics in GNNs
Get familier with
Node embedding methods
Graph regularization and connectivity
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners interested in applying mathematical principles to graph-based problems in social networks, biological systems, or recommendation engines—ideal for those pursuing specialized AI expertise
Prerequisites
Basic linear algebra and graph theory concepts.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Basic linear algebra and graph theory concepts.
Who Should Enroll?
This course is for learners interested in applying mathematical principles to graph-based problems in social networks, biological systems, or recommendation engines—ideal for those pursuing specialized AI expertise
By end of this course
Get Stronger in
- Graph Laplacians and adjacency matrices
- Message passing mechanics in GNNs
Get Familiar in
- Node embedding methods
- Graph regularization and connectivity
Course Contents
Topics
- Adjacency matrices and Laplacians
- Degree and normalized Laplacians
- Spectral decomposition of graphs
Key Outcomes
Build mathematical foundation for GNNs
Topics
- GCN layer derivation
- Message passing math
- Graph convolution as spectral filtering
- Normalization tricks
Key Outcomes
Derive and train graph based models
Topics
- Latent variable models
- Evidence Lower Bound (ELBO) derivation
- Reparameterization trick math
Key Outcomes
Understand and implement VAEs
Topics
- Adjacency matrices and Laplacians
- Degree and normalized Laplacians
- Spectral decomposition of graphs
Key Outcomes
Build mathematical foundation for GNNs
Topics
- GCN layer derivation
- Message passing math
- Graph convolution as spectral filtering
- Normalization tricks
Key Outcomes
Derive and train graph based models
Topics
- Latent variable models
- Evidence Lower Bound (ELBO) derivation
- Reparameterization trick math
Key Outcomes
Understand and implement VAEs