albertonieto44 / ml-class-anomaly-detection

Course materials for Algorithms in Machine Learning: Anomaly Detection at ISAE-SUPAERO.

Anomaly Detection

Algorithms in Machine Learning, ISAE-SUPAERO

This repository contains course materials for my Machine Learning class on the topic of Anomaly Detection:

• anomaly_detection_class.pdf : course presentation with teaching content
• anomaly_detection_class.ipynb : Python notebook illustrating the course notions
• anomaly_detection_class-empty.ipynb : same Python notebook to follow with students during class

An application exercise is also available:

• anomaly_detection_exercise.pdf : instructions for the exercise
• data/dataset.csv : the dataset to be used to answer the questions

Option 1: Working with Google Colab

To follow the notebooks with Google Colab, simply go to https://colab.research.google.com/. Import a new notebook from GitHub, search for "jfabrice" and open one of the notebooks of this class (ml-class-anomaly-detection), for example anomaly_detection_class-empty.ipynb. Then click on "Copy to Drive" to be able to execute it. The first section of the notebook is there to initialize the environment from Google Colab.

Option 2: Working locally - Setting up Anaconda environment

To setup the Anaconda environment with required dependencies, execute the following instructions in Anaconda prompt or Linux shell.

# Clone this github repository on your machine
git clone https://github.com/jfabrice/ml-class-anomaly-detection.git

# Change working directory inside the repo
cd ml-class-anomaly-detection

# Create a new virtual environment
conda create -n anomalydetectionenv python==3.6

# Activate the environment
## For Linux / MAC
source activate anomalydetectionenv
## For Windows
activate anomalydetectionenv

# Install the requirements
pip install -r requirements.txt