Descriptif

Machine learning is an increasingly important area, and it has provided many of the recent advances behind applications of artificial intelligence. It is relevant to a plethora of application domains in science and industry including in finance, health, transport, linguistics, media, and biology.

Lectures will cover the most important concepts and algorithms. We will cover in some degree all the main paradigms of machine learning: supervised learning (regression, classification), unsupervised learning, and reinforcement learning. Among many learning algorithms we will look at:

• least squares regression,
• logistic regression,
• k-nearest neighbors,
• neural networks and deep learning,
• decision tree inducers and ensemble methods,
• principal components analysis,
• k-means clustering
• kernel methods
• Q-learning.

In the labs, we will implement many of these and investigate their use in different applications. Programming will be done in Python with scientific libraries such as numpy and scikit-learn.

The main grading component is a team project, along with two in-class tests.