The module gives a theoretical overview of the various forms of machine learning. First, we have a glance at unsupervised techniques that help us understand the data. Then, we focus on various supervised learning systems used in practice such as linear models, non linear models (in particular, neural networks), non parametric models, and support vector machines. We show how ensembles of models can outperform a single model, and we detail the particular case of deep neural networks and deep learning. Finally, we study how agents can learn what to do in the absence of labeled examples of what to do. We see how agents can learn from past experience to change their behavior using reinforcement learning techniques (Q-learning). Labs will be performed using python libraries like scikit-learn, pandas or numpy.
This teaching unit counts for 3 credits (/30 total for a semester).
| On completion of the unit, the student will be capable of: | Classification level | Priority |
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| Type of teaching activity | Content, sequencing and organisation |
|---|---|
| Lectures | (10h) |
| Supervised work | (10h) |
| Labs | (10h) |