Course Materials
Educational resources for teaching and learning network machine learning
Machine Learning for Computer Systems
Our primary course on applying machine learning to computer systems and networking.
Course Website: noise-lab.net/ml-systems
Course Overview
This course covers applications of machine learning to computer systems, with a particular focus on security, performance analysis, and prediction problems in networking. Topics include data preparation, feature selection, model evaluation, fairness, interpretability, and ML testing/debugging.
Resources
- GitHub Repository: github.com/noise-lab/ml-systems
- Jupyter Notebooks: Interactive lectures covering each topic
- Board Notes (PDF): Dropbox
- ML Readings: noise-lab.net/ml-systems/ml.html
Topics Covered
- Network traffic analysis use cases
- Data acquisition and feature extraction
- Supervised learning (Naive Bayes, regression, SVMs, trees, deep learning)
- Unsupervised learning (clustering, dimensionality reduction, autoencoders)
- Generative models (transformers, diffusion models)
- Timeseries analysis and reinforcement learning
- Model performance and maintenance
Network Measurements
This course covers network measurements, including methodologies, tools, and applications for understanding network behavior and performance.
Course Website: fbronzino.notion.site/network-measurements
Course Overview
This course focuses on network measurements broadly speaking. As machine learning becomes increasingly key for understanding our networks, a good portion of the course focuses on these applications. Students learn how to collect, analyze, and interpret network data, with special emphasis on modern ML-based approaches for network inference and analysis.
Resources
- Video Quality Inference Lab: github.com/wontoniii/video_inference
- Systems Costs and Model Performance Lab: github.com/wontoniii/representations_cost
Additional Resources
Assignments
Problem sets and programming assignments designed to provide hands-on experience with ML for networking applications.
Reading Materials
Recommended textbooks, research papers, and supplementary reading materials.