ML-NET
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.
Lectures (notation used):
- Introduction: slides; video; code
- Regression: slides; video; code
- Regression (continued) and Classification: slides video ; code
- Neural Networks slides video; code
- Trees and Ensembles slides video; code
- Unsupervised Learning: Clustering and Anomaly Detection slides video; code
- Dimensionality reduction for Anomaly Detection and Supervised Learning code
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.
English -> French ML dictionary: here
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.