Franck Iutzeler

Final Projects

Below are described the proposed projects, you are also free to setup your own. The description of the projects is voluntarily vague and initiative is expected. You are expected to look for information, tips, etc.

For all projects, you have to (and will be graded on these points):

In practice:

Project examples

Project 1: Tree-based classifiers

Tree-based classifiers are classification procedures that determine a class by a succession of tests. For that reason, it is widely used in the industry. However, it raises a number of questions in terms of learning performance. Scikit-learn's documentation is well done for this problem.

Example of goals:

Project 2: MultiClass Prediction

Classifying into more than two classes may be way more involved than in the binary case. Worse, the imbalance between the number of examples in each class may become a serious problem (you can find examples of datasets here and here). Compare different strategies (like One versus All, Tree based approaches, etc.) and ivestigate different scoring metrics.

Example of goals:

Project 3: Deep Learning with TensorFlow/Keras

In the last years, deep learning methods have become more and more popular especially as they reached mind-blowing precision on machine learning tasks such as image classification. Most frameworks for neural networks are interfaced with Python, the most popular being Google's TensorFlow and Keras. A typical good case for neural networks is image recognition.

Example of goals:

Project 4: Kaggle

Take a Kaggle problem you find interesting and try to reach a good score. For instance, taxi fare prediction, box office, no show in appointments are good choices, with lots of discussion on the data. However, you can choose your own.

Example of goals:

Project 5: Visualization

We have seen matplotlib or seaborn, but many other exist such as plotnine/ggplot or bokeh It is also possible to make user interfaces, using eg. tkinter (see for instance here)

Example of goals: