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MachineLearningForNetworks

The goal of this 45h-course is to understand the basics of Machine Learning and apply it to problems in Computer Networks.

Equal space is given to theory and practice.

Core Lectures (slides, videos and code are provided in github):

1. Introduction
2. Regression
3. Regression (continued) and Classification
4. Neural Networks
5. Trees and Ensembles
6. Unsupervised Learning: Clustering and Anomaly Detection
7. Dimensionality reduction for Anomaly Detection and Supervised Learning

Lectures provided by external experts

• Dimensionlity Reduction and Network Anomalies
• Machine Data Analysis with Elastic Stack
• Precictive Maintenance
• Machine Learning for High Speed Networks
• Collecting and processing Network Data
• Intrusion Detection from Network Traces
• QoE inference for Video on Demand

Lectures skipped this year:

• Recommender Systems slides

Python code

• Python notebooks, ready to run on Google Colab can be found in my github page.
• All you need is any browser and a google drive account.

All the material, the code and the slides, are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (see the terms): you are free to re-use them, but you must cite the autor and this original work.