EPFL-Projects2 Solved

Projects

Machine Learning Course

EPFL

Introduction
In this project, you will learn to use the concepts we have seen in the lectures and practiced in the labs on a real-world dataset, start to finish. You will be doing exploratory data analysis to understand your dataset and your features, do feature processing and engineering to clean your dataset and extract more meaningful information, implement and use machine learning methods on real data, analyze your model and generate predictions using those methods and report your findings.

Option A - Machine Learning for Science
Pick a real-world challenge offered by any research group of the (extended) EPFL campus, subject to availability. You learn about an interesting application domain, and collaborate with the lab to apply machine learning methods to their specific research question, on real data provided by the lab - an exciting option to follow an interdisciplinary approach to find new insights with your team.

           (More details and grading criteria further down)

•    Written Report. You will write a maximum 4 page PDF report on your findings, using LaTeX.

•    Code. In Python. External libraries are allowed, if properly cited.

The guidelines for the projects are the same as in the standard tasks explained below.

Participation of the Hosting Lab. The only major difference to the other options is that here, you do this project in collaboration with another lab on EPFL campus. The lab hosting you will help us grading the domainspecific merit of your contribution. We encourage you to reach out to any lab of your choice at EPFL, UniL, CERN, CHUV, Idiap etc., and ask them for a project idea, or propose an idea to them.

Here is a list of labs which already have proposed several exciting project ideas. Note that other labs are possible as well.

https://docs.google.com/spreadsheets/d/1Mav7vND2dYghHQxLEXqnvLZ1z9GKRcNynOAjwcQpMPo/ Important Logistics: You can only sign up for this option if the professor of that lab agrees to host your group of 3 students, and will confirm this by filling the mandatory registration form for labs, by November 15th:

https://goo.gl/forms/0ElZtbKGz4U0lCag1

Submission URL for the final project: http://mlcourse.epfl.ch (same as for option B)

Option B - One of our Pre-defined Challenges
Deliverables at a glance.            (More details and grading criteria further down)

•    Written Report. You will write a maximum 4 page PDF report on your findings, using LaTeX.

•    Code. In Python. External libraries are allowed, if properly cited.

•    Competitive Part. To give you immediate feedback and a fair ranking, we use the competition platform AIcrowd.com to score your predictions. You can submit whenever and almost as many times as you like, up until the final submission deadline.

Pick Your Favorite Among Three Challenges. Pick your favorite competition among the following three online competitions. Don’t be influenced by their seemingly different difficulty level, since your contribution as compared to standard approaches will be taken into account in the grading.

Step 1 - Getting started
In order to be able to access the challenges, please first create an account at AIcrowd.com using your @epfl.ch email address. Pick your favorite competition among the following three. To read the description and download the dataset, please follow the corresponding links:

https://www.aicrowd.com/challenges/epfl-ml-text-classification-2019 https://www.aicrowd.com/challenges/epfl-ml-road-segmentation-2019 https://www.aicrowd.com/challenges/epfl-ml-recommender-system-2019

For all three possible tasks, we provide some additional description and sample code on the course github:

https://github.com/epfml/ML_course/tree/master/projects/project2

Step 2 - Implement ML Methods
You are allowed to use any external library and ML techniques, as long as you properly cite any external code used.