Preparation for the Workshop

Please complete the following steps before the beginning of the workshop on Saturday, January 14. The goal is to familiarize yourself with a toolkit that is suitable for machine learning methods, the extraction of knowledge from data sets, and to set up a working programming environment that has the Python packages we will need at its disposal.

Pre-Work Office Hour

If you have any questions about or issues with the tasks described below, please visit our (optional) office hour on
Friday, January 13 at 8:00 PM ET/ 7:00 PM CT.

Location: Gather room of the workshop.

Do these things until the workshop on Saturday, January 14:

1. Revisit what you learnt about linear and logistic regression in the Statistical Methods Workshop on December 3 and 4. What is the difference between linear and logistic regression?

2. Download the Jupyter notebook session0a_test_environments.ipynb (also available in this OneDrive). Run this notebook to install the Python packages that we will need throughout the workshop. It will also down a dataset in a folder called FashionMNIST which we will need later.
   
   If you run into any issues when trying to execute the notebook, you can use the environment file environment.yml and create a new conda environment.
   This can be done by navigating the terminal / Anaconda Prompt to the folder which contains the environment.yml file and using the commands
   conda env create --name inmas-ml --file environment.yml
conda activate inmas-ml.

3. Familiarize yourself with the scikit-learn package by completing the two (small) tasks in the Jupyter notebook session0b_prework.ipynb (also in OneDrive). The following class might be helpful:

• sklearn.linear_model.LinearRegression, cf. scikit-learn documentation.
  
  In this workshop and in the machine learning practice in general, the concept of class objects and inheritance will be used.
  If you are not yet familiar, you can read up on what this means here.

Solution Keys

00_prework_sol.ipynb