spantiru/companion-lab

JupyterLab for the Machine Learning seminar

JupyterLab for the Machine Learning seminar

Ștefan Panțiru, Faculty of Computer Science, "Alexandru Ioan Cuza" University Iași

To run this lab, either click the launch binder button above, or run the code on your machine following the instructions below.

Contents

• Lab01 - Elementary Notions in Probability and Statistics
• Lab02 - Decision Trees (part1)
• Lab03 - Decision Trees (part2)
• Lab04 - Decision Trees (part3)
• Lab05 - Naive Bayes (part1)
• Lab06 - Naive Bayes (part2)
• Lab07 - Maximum Likelihood Estimation
• Week 8 - Midterm Exam
• Lab09 - Logistic Regression
• Lab10 - k-Nearest Neighbour
• Lab11 - AdaBoost
• Lab12 - Hierarchical Clustering
• Lab13 - k-Means (part1)
• Lab14 - k-Means (part2)

Useful resources:

• ML Homework
• Register for Piazza
• Resources uploaded to Piazza
• Python official documentation
• Scikit-learn library - machine learning library for Python
• Scipy statistical functions
• Pandas library - library providing data structures and analysis tools for Python

Running locally

This lab uses Python 3.12 and pipenv, so make sure they are available on your system.

$ python3 --version
Python 3.12.3
$ python3 -m pipenv --version
pipenv, version 2023.12.1

Clone the repository using git:

$ git clone https://github.com/spantiru/companion-lab.git

Inside the project folder, create the pipenv environment:

$ cd companion-lab
$ python3 -m pipenv install # Might take a few seconds

Run jupyter-lab, which should start in your default browser:

$ python3 -m pipenv run jupyter-lab