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ml-project-1

Dream Team project

Authors

  • Thierry Bossy
  • Raphael Strebel
  • Ignacio S. Aleman

Description

Project 1 of the "Machine Learning" course (CS433), given in the Fall semester of 2019.

Instructions

Data

To run our project, create a folder named "data" at the root of the project directory and put the test.csv and train.csv files in it.

Execution

Navigate to "src" and run python run.py. (Note that you need at least Python 3.6 to run the code)

Code structure

Methods in implementations.py use different cost functions or gradient computation that can be found in cost.py. A big generic gradient descent method is implemented and used by all gradient descent models with different parameters.

Definition of gradient_descent method: def gradient_descent(y, tx, compute_loss, compute_gradient, initial_w, max_iters=0, gamma=10e-6, batch_size=None, num_batch=None, debugger=None, dynamic_gamma=False):

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Project 1 of the graduate ML course at EPFL (predicting the Higgs Boson on the CERN data set)

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