Machine Learning for Networking

This repository contains exercises, resources, and information for learning machine learning concepts and applying them to networking.

Videos

• Networking videos, which provide necessary networking background. You are strongly encouraged to develop a firm understanding of networking, as this course will apply concept
• Machine learning videos provide backgound on machine learning concepts.
• The course wiki has more resources, including links to past projects, readings, and project ideas.

Structure

A general structure for a short course on this material is as follows. Each lecture has (1) a set of notes and slides explaining concepts, (2) an accompanying Jupyter Notebook with example applications from networking to make the concepts more concrete and further expand on networking concepts.

Module	Topic	Activity
1	Overview and Motivation	Python Basics
2	Measurement: Data and Feautres	Packet Capture
3	Machine Learning Pipelines	Pipelines and Model Selection
4	Linear Regression	IoT: Energy Prediction
5	Logistic Regression	DNS Query Detection
6	Naive Bayes	Spam Filtering
7	Trees and Ensembles	Activity Recognition
8	Deep Learning	DDoS Detection
9	Unsupervised Learning	Traffic Clustering
10	Automated Machine Learning	nprintML

All of the notebooks, notes, and slides for each lecture can be found in the respective directory.

Repository

The repository has the following organization:

• slides/ - slides for each lecture
• notes/ - notebooks for each lecture
• activities/ - notebooks for each lecture