bostdiek / kias_winter_school

KIAS Winter School - Intro to Machine Learning

Technical Details

To use the tutorials, you will either need to have a python environment already setup with the packages from the requirements.txt, along with Tensorflow. If you do not have access to these, my environment, including python and jupyter notebooks can be downloaded using Docker. Please have either a working environment or Docker already installed before the school. If using docker, please also run the following code before the first tutorial to download and install all of the packages. We will use the same call from the terminal each time we want to open the notebooks, but it will only download and install the first time.

Run the notebooks using:

docker run -p 8888:8888 -it -v $PWD:$PWD -w $PWD -e JUPYTER_ENABLE_LAB=yes bostdiek/kias_ws

To quit the notebooks, select File > Shutdown or hit Control + C in the terminal window.

To download the turorials, use git clone https://github.com/bostdiek/KIAS_Winter_School.git. Then move into that directory and launch the notebooks: docker run -p 8888:8888 -it -v $PWD:$PWD -w $PWD -e JUPYTER_ENABLE_LAB=yes bostdiek/kias_ws

Repository Structure

├── Dockerfile
├── LICENSE
├── README.md
├── data
│   ├── top_tagging
│   │   ├── raw
│   │   │   ├── test.h5
│   │   │   ├── train.h5
│   │   │   └── val.h5
│   │   └── smaller_raw
│   │       ├── nsubjettiness_test.npy
│   │       ├── nsubjettiness_training.npy
│   │       ├── nsubjettiness_val.npy
│   │       ├── test_events.npy
│   │       ├── test_images.npy
│   │       ├── test_labels.npy
│   │       ├── training_events.npy
│   │       ├── training_images.npy
│   │       ├── training_labels.npy
│   │       ├── val_events.npy
│   │       ├── val_images.npy
│   │       └── val_labels.npy
│   ├── tutorial_1_data
│   │   ├── linear_regression_curved_test.npy
│   │   ├── linear_regression_curved_training.npy
│   │   ├── linear_testing.npy
│   │   ├── linear_training.npy
│   │   ├── logistic_regression_testing.npy
│   │   ├── logistic_regression_training.npy
│   │   └── logistic_regression_validation.npy
│   └── tutorial_2_data
│       ├── gluons.csv
│       └── quarks.csv
├── requirements.txt
├── slides
│   ├── KIAS_Ostdiek_MachineLearning_1.key
│   ├── KIAS_Ostdiek_MachineLearning_1.pdf
│   ├── KIAS_Ostdiek_MachineLearning_2.key
│   ├── KIAS_Ostdiek_MachineLearning_2.pdf
│   ├── KIAS_Ostdiek_MachineLearning_3.key
│   └── KIAS_Ostdiek_MachineLearning_3.pdf
└── tutorials
    ├── Tutorial1.ipynb
    ├── Tutorial1_Answers.ipynb
    ├── Tutorial2.ipynb
    ├── Tutorial2_Answers.ipynb
    ├── Tutorial3_0_TopTagging_ProcessRawData.ipynb
    └── Tutorial3_1_TopTagging_Visualizations.ipynb