Notebooks, slides and dataset of the CorrelAid Machine Learning Winter School

Overview

CorrelAid Machine Learning Spring School

Welcome to the CorrelAid ML Spring School!

In this repository you can find the slides and other files for the CorrelAid ML Spring School. The following sections become relevant as the course progresses.

Task

The problem we want to solve is to classify trees in Roosevelt National Forest.

Setup

Please make sure you have a modern Python 3 installation. We recommend the Python distribution Miniconda that is available for all OS.

The easiest way to get started is with a clean virtual environment. You can do so by running the following commands, assuming that you have installed Miniconda or Anaconda.

$ conda create -n spring-school python=3.9
$ conda activate spring-school
(spring-school) $ pip install -r requirements.txt
(spring-school) $ python -m ipykernel install --user --name spring-school --display-name "Python 3.9 (spring-school)"

The first command will create a new environment with Python 3.9. To use this environment, you call conda activate <name> with the name of the environment as second step. Once activated, you can install packages as usual with the pip package manager. You will install all listed requirements from the provided requirements.txt as a third step. Finally, to actually make your new environment available as kernel within a Jupyter notebook, you need to run ipykernel install, which is the fourth command.

Once the setup is complete, you can run any notebook by calling

(spring-school) $ <jupyter-lab|jupyter notebook>

jupyter lab is opening your browser with a local version of JupyterLab, which is a web-based interactive development environment that is somewhat more powerful and more modern than the older Jupyter Notebook. Both work fine, so you can choose the tool that is more to your liking. We recommend to go with Jupyter Lab as it provides a file browser, among other improvements.

If you encounter any difficulties while installing, please contact Daniel, Pia or Flo.

Data

The data to be analyzed is one of the classic data sets from the UCI Machine Learning Repository, the Forest Cover Type Dataset.

The dataset contains tree observations from four areas of the Roosevelt National Forest in Colorado. All observations are cartographic variables (no remote sensing) from 30 meter x 30 meter sections of forest. There are over half a million measurements total!

The dataset includes information on tree type, shadow coverage, distance to nearby landmarks (roads etcetera), soil type, and local topography.

Note: We provide the data set as it can be downloaded from kaggle and not in its original form from the UCI repository.

Attribute Information:

Given is the attribute name, attribute type, the measurement unit and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database.

Name / Data Type / Measurement / Description

  • Elevation / quantitative /meters / Elevation in meters
  • Aspect / quantitative / azimuth / Aspect in degrees azimuth
  • Slope / quantitative / degrees / Slope in degrees
  • Horizontal_Distance_To_Hydrology / quantitative / meters / Horz Dist to nearest surface water features
  • Vertical_Distance_To_Hydrology / quantitative / meters / Vert Dist to nearest surface water features
  • Horizontal_Distance_To_Roadways / quantitative / meters / Horz Dist to nearest roadway
  • Hillshade_9am / quantitative / 0 to 255 index / Hillshade index at 9am, summer solstice
  • Hillshade_Noon / quantitative / 0 to 255 index / Hillshade index at noon, summer soltice
  • Hillshade_3pm / quantitative / 0 to 255 index / Hillshade index at 3pm, summer solstice
  • Horizontal_Distance_To_Fire_Points / quantitative / meters / Horz Dist to nearest wildfire ignition points
  • Wilderness_Area (4 binary columns) / qualitative / 0 (absence) or 1 (presence) / Wilderness area designation
  • Soil_Type (40 binary columns) / qualitative / 0 (absence) or 1 (presence) / Soil Type designation
  • Cover_Type (7 types) / integer / 1 to 7 / Forest Cover Type designation

CC BY 4.0

Owner
CorrelAid
Soziales Engagement 2.0 - Datenanalyse für den guten Zweck
CorrelAid
log4j-tools: CVE-2021-44228 poses a serious threat to a wide range of Java-based applications

log4j-tools Quick links Click to find: Inclusions of log4j2 in compiled code Calls to log4j2 in compiled code Calls to log4j2 in source code Overview

JFrog Ltd. 171 Dec 25, 2022
This repository consists of the python scripts for execution and automation of vivid tasks.

Scripting.py is a repository being maintained to keep log of the python scripts that I create for automating and executing some of my boring manual task.

Prakriti Regmi 1 Feb 07, 2022
zip-brute Zip File Password Cracking with Using Password List

Zip brute is a python script that cracks zip that are password protected using a wordlist dictionary.

AnonyminHack5 13 Nov 03, 2022
A toolkit for web reconnaissance, it's fast and easy to use.

A toolkit for web reconnaissance, it's fast and easy to use. File Structure httpsuite/ main.py init.py db/ db.py init.py subdomains_db directories_db

whoami security 22 Jul 22, 2022
Fuzzercorn - Bring libfuzzer to Unicorn

Fuzzercorn libfuzzer bindings for Unicorn. API // The main entry point of the fu

lazymio 23 Nov 17, 2022
Bug Alert: a service for alerting security and IT professionals of high-impact and 0day vulnerabilities

Bug Alert Bug Alert is a service for alerting security and IT professionals of h

BugAlert.org 208 Dec 15, 2022
Solución al reto BBVA Contigo, Hack BBVA 2021

Solution Solución propuesta para el reto BBVA Contigo del Hackathon BBVA 2021. Equipo Mexdapy. Integrantes: David Pedroza Segoviano Regina Priscila Ba

Gabriel Missael Barco 2 Dec 06, 2021
Small python script to look for common vulnerabilities on SMTP server.

BrokenSMTP BrokenSMTP is a python3 BugBounty/Pentesting tool to look for common vulnerabilities on SMTP server. Supported Vulnerability : Spoofing - T

39 Dec 16, 2022
Hack computer in the form of RAR files from all types of clients, even Linux

Program Features 📌 Hide malware 📌 Vulnerability software vulnerabilities RAR 📌 Creating malware 📌 Access client files 📌 Client Hacking 📌 Link Do

hack4lx 5 Nov 25, 2022
Ini membuat tema berbasis bendera Indonesia with Python + Linux.py

tema Ubah Tema Termux Menjadi Linux Ubah Font Termux Jadi Linux dibuat oleh wahyudioputra INSTALL pkg update && pkg upgrade pkg install python pkg ins

wahyudioputra 2 Nov 30, 2021
macOS Initial Access Payload Generator

Mystikal macOS Initial Access Payload Generator Related Blog Post: https://posts.specterops.io/introducing-mystikal-4fbd2f7ae520 Usage: Install Xcode

Leo Pitt 206 Dec 31, 2022
HatSploit native powerful payload generation and shellcode injection tool that provides support for common platforms and architectures.

HatVenom HatSploit native powerful payload generation and shellcode injection tool that provides support for common platforms and architectures. Featu

EntySec 100 Dec 23, 2022
CC CAMERA HACKING TOOL

CAM-HACK CC CAMERA HACKING TOOL Installation On Termux $ apt update

Aryan 10 Sep 25, 2022
Exploit-CVE-2021-21086

CVE-2021-21086 Exploit This exploit allows to execute a shellcode in the context of the rendering process of Adobe Acrobat Reader DC 2020.013.20074 an

Faraday 23 Nov 09, 2022
Script for automatic dump and brute-force passwords using Volatility Framework

Volatility-auto-hashdump Script for automatic dump and brute-force passwords using Volatility Framework

whoamins 11 Apr 11, 2022
Convert a collection of features to a fixed-dimensional matrix using the hashing trick.

FeatureHasher Convert a collection of features to a fixed-dimensional matrix using the hashing trick. Note, this requires Jina=2.2.4. Example Here I

Jina AI 5 Mar 15, 2022
Python implementation for CVE-2021-42278 (Active Directory Privilege Escalation)

Pachine Python implementation for CVE-2021-42278 (Active Directory Privilege Escalation). Installtion $ pip3 install impacket Usage Impacket v0.9.23 -

Oliver Lyak 250 Dec 31, 2022
A honeypot for the Log4Shell vulnerability (CVE-2021-44228)

Log4Pot A honeypot for the Log4Shell vulnerability (CVE-2021-44228). License: GPLv3.0 Features Listen on various ports for Log4Shell exploitation. Det

Thomas Patzke 79 Dec 27, 2022
log4j2 passive burp rce scanning tool get post cookie full parameter recognition

log4j2_burp_scan 自用脚本log4j2 被动 burp rce扫描工具 get post cookie 全参数识别,在ceye.io api速率限制下,最大线程扫描每一个参数,记录过滤已检测地址,重复地址 token替换为你自己的http://ceye.io/ token 和域名地址

5 Dec 10, 2021
ORector - A Fast Python tool designed to detect open redirects vulnerabilities on websites

ORector is a Fast Python tool designed to detect open redirects vulnerabilities

11 Apr 02, 2022