Statistics and Machine Learning in Python

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Structure

Courses are available in three formats:

1. Jupyter notebooks */*.ipynb files.

2. Python files using sphinx-gallery */*.py files.

3. ReStructuredText files.

All notebooks and python files are converted into rst format and then assembled together using sphinx.

Directories and main files:

introduction/
├── machine_learning.rst
└── python_ecosystem.rst

python_lang/                        # (Python language)
├── python_lang.py # (main file)
└── python_lang_solutions.py

scientific_python/
├── matplotlib.ipynb
├── scipy_numpy.py
├── scipy_numpy_solutions.py
├── scipy_pandas.py
└── scipy_pandas_solutions.py

statistics/                         # (Statistics)
├── stat_multiv.ipynb               # (multivariate statistics)
├── stat_univ.ipynb                 # (univariate statistics)
├── stat_univ_solutions.ipynb
├── stat_univ_lab01_brain-volume.py # (lab)
├── stat_univ_solutions.ipynb
└── time_series.ipynb

machine_learning/                   # (Machine learning)
├── clustering.ipynb
├── decomposition.ipynb
├── decomposition_solutions.ipynb
├── linear_classification.ipynb
├── linear_regression.ipynb
├── manifold.ipynb
├── non_linear_prediction.ipynb
├── resampling.ipynb
├── resampling_solution.py
└── sklearn.ipynb

optimization/
├── optim_gradient_descent.ipynb
└── optim_gradient_descent_lab.ipynb

deep_learning/
├── dl_backprop_numpy-pytorch-sklearn.ipynb
├── dl_cnn_cifar10_pytorch.ipynb
├── dl_mlp_mnist_pytorch.ipynb
└── dl_transfer-learning_cifar10-ants-

Installation for students

Install Anaconda at https://www.anaconda.com/ with python >= 3.

Standard user (student) should install the required data analysis packages. Create and activate the pystatsml_student environment:

conda env create -f environment_student.yml
conda activate pystatsml_student

Installation for teachers: to build the documents

Expert users (teachers) who need to build (pdf, html, etc.) the course should install additional packages including:

• pandoc
• sphinx-gallery
• nbstripout

Create and activate the pystatsml_teacher environment:

conda env create -f environment_teacher.yml
conda activate pystatsml_teacher

Build the documents. Configure your git repository with nbstripout: a pre-commit hook for users who don't want to track notebooks' outputs in git.

nbstripout --install

Optional: install LaTeX to generate pdf. For Linux debian like:

sudo apt-get install latexmk texlive-latex-extra

After pulling the repository execute Jupyter notebooks (outputs are expected to be removed before git submission):

make exe

Build the pdf file (requires LaTeX):

make pdf

Build the html files:

make html

Clean everything:

make clean

Optional to generate Microsoft docx. Use docxbuilder:

make docx