Descriptif

COURSE PHILOSOPHY:

The amount of data produced in environmental sciences (energy and climate prediction) paves
the way to new applications. While traditionnal statistical methods are still essential, advanced
machine-learning methods are more and more needed to make sense of such big data, whether
it is for analysis or to make predictions. One goal of machine learning is to extract identifiable
patterns from these complex data sets. These patterns can then be used to take informed
decisions. Another is to model relationships between different variables and then use these
models to predict one variable from information of the other.
Examples of such big data sets are

• If we want to optimize the energy performance of a building, we can set sensors in differ-
ent places of the building that will give us a good overview of the energy consumption
and energy loss. Analyzing such data set with machine learning will help us predict or
optimize our energy consumption.
• The IPCC provides forecast of the average temperature for the next 100 years. This
forecast is based on about 30 different predictions made by complex models. Machine
learning provides a way to trak reliable patterns in this complex data set

The objective of this course is to provide an introduction to statistical analysis and machine
learning in order to help the students apply relevant methods to analyze specific data sets.
Different families of unsupervised and supervised methods will be covered, while also proving a
general approach to validate and test results. We encourage students to develop their critical
thinking skills when facing a new dataset and applying a method in order to draw robust
conclusions. We illustrate this course with examples from environmental sciences. These
datasets correspond to concrete cases of analysis presented in the form of ipython notebook.

TOPICS COVERED:

1. Introduction
2. Supervised learning problem
3. Bias, variance and validation
4. Regularization
5. Classification
6. Unsupervised learning
7. Ensemble methods
8. Neural network I
9. Neural network II

Prerequisites (Niveau requis) : Python and elements of probabilities

Grading Policy (Modalités d'évaluation) : 25% participation, 25% final notebook, 50% final presentation

Langue du cours : Anglais