Hebrew University, Jerusalem, Israel

An introductory code to the world of machine- and statistical learning, aimed for undergraduate students of computer science. The following repository contains:

1. Course Book - Based on lecture- and recitation notes
2. Code examples and graphs generating code, used throughout the book
3. Hands-on guided labs to experience aspects discussed in lectures and recitations
4. Skeleton files for software package IMLearn developed throughout the course
5. Skeleton files for course exercises

Set a local copy of this GitHub repository. Do so by forking and cloning the repository or cloning the repository using GitBash and

cd LOCAL_REPOSITORY_PATH
git clone https://github.com/GreenGilad/IML.HUJI.git

or by downloading and unzipping it in LOCAL_REPOSITORY_PATH. Then:

• Download and install Anaconda from official website.
• Verify instllation by starting the Anaconda Prompt. A terminal should start with the text (base) written at the beginning of the line.
• Set the IML conda environment. Start the Anaconda Prompt and write:

  conda env create -f "LOCAL_REPOSITORY_PATH\environment.yml"
  

  This will create a conda envoronment named iml.env with the specifications defined in environment.yml. If creating failed due to ResolvePackageNotFound: plotly-orca remove this line from environment file, create environment without, and then after activating environment run:

  conda install -c plotly plotly-orca
  

• Activate the environment by conda activate iml.env.
• To open one of the Jupyter notebooks:

  jupyter notebook "LOCAL_REPOSITORY_PATH\lab\Lab 00 - A - Data Analysis In Python - First Steps.ipynb"
  

Another option is to run the Jupyter notebooks through the PyCharm IDE plug-in.

• Install the PyCharm IDE (professional edition) as described on the Install PyCharm page. Be sure to install the Jupyter plug-in.
• Follow the Configure a Conda virtual environment page.
• Open a PyCharm project from LOCAL_REPOSITORY and set the project's interpreter to be the iml.env environment.

One can also view and run the labs and code examples via Google Colab. It supports loading and running Jupyter notebooks and running using a specified Conda environemnt.