ml-systems
Machine Learning for Computer Systems
View the Project on GitHub
noise-lab/ml-systems
Machine Learning for Networking
Background Readings and Videos
Survey 1
Survey 2
Graduate Course
Networking videos
Datasets
CIC Network Intrusion Detection Data (CICIDS)
UC Irvine IDS/Botnet Dataset
TLS Handshake Dataset
NetML Challenge and Datasets (2020)
MAWI Traffic Traces
Tools
nPrint Traffic Fingerprinting
netML Network Traffic Representation
Wireshark
Supervised Learning
Splines
Bias, Variance, and Regularization in Linear Regression
Regularization
Intro to Naïve Bayes
Naïve Bayes with Jupyter Notebook Example
Naïve Bayes (YouTube)
SVM Intro (YouTube, Stanford)
SVM Intro (YouTube, Statquest)
Random Forest Background (YouTube)
Random Forest/Ensemble Background
Advantages/Disadvantages of RF
Hidden Markov Models (YouTube)
Neural Network Playground
Unsupervised Learning
Choosing k in k-means
Intro to KDE (YouTube)
Explanation of Density Estimation from sklearn docs
1D KDE Clustering Example
Mixture Models Background
Intro to EM (YouTube)
EM Intro
Using PCA, Visualizing PCA Results
How to Read PCA Biplots, Scree Plots
PCA and SVD Explained with Numpy
Deep Learning
Large Language Models
Visualization and Interpretation
Formatting Seaborn Axis/Tick Labels
Colormaps for Clustering
Train/Test Split
Helpful Notebooks
Data Science in Python Cookbook
Variety of Topics
ML from Scratch
Other
Princeton ML Course
Fourier Transform
Human in the Loop
Data Science Ethics Checklist