About the course
How do diseases and information spread? How can we predict traffic or weather? Answering these questions requires a great deal of data. Complex data can be represented as a graph of relationships and interactions between objects. Graph data structures can be ingested by algorithms such as neural networks to perform tasks including classification, clustering, and regression. This course explores the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By studying underlying graph structures, you will learn machine learning and data mining techniques that can improve prediction and reveal insights on a variety of networks.
What you will learn
- Graph neural networks
- Representation learning
- Node embeddings and classification
- Link analysis for networks
- Graph structure of the web
- Models of network evolution and network cascades
- Reasoning over knowledge graphs
- Deep generative models for graphs
- Influence maximization in networks
- Communities and clusters in networks
- Proficiency in Python: All coding assignments will be written in Python. You should be familiar with numpy and matplotlib, as well as basic shell commands (ssh, scp, ls, cd, rm, mv, cp, zip, etc.).
- Calculus and Linear Algebra: You should understand the following concepts from multivariable calculus and linear algebra: chain rule, gradients, matrix multiplication, matrix inverse.
- Probability: You should be familiar with basic probability distributions and be able to define the following concepts for both continuous and discrete random variables: Expectation, independence, probability distribution functions, and cumulative distribution functions.
- Foundations of Machine Learning (Recommended): Knowledge of basic machine learning and/or deep learning is helpful, but not required.
This professional online course, based on the Spring 2021 Stanford graduate course CS224W, features:
- Lecture videos edited and segmented to focus on essential content
- Coding assignments in which you will apply course topics to real-life models
- Office hours and support from Stanford-affiliated Course Facilitators
- Cohort structure providing opportunities to network and collaborate with motivated learners from diverse locations and professional backgrounds
Leskovec's research focuses on the analyzing and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and v...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.