À la fin de ce cours, vous saurez :
This course introduces the basic notions of Machine Learning, ranging from Shallow to Deep Learning models.
Machine Learning is a large branch of Artificial Intelligence dealing with the problem of designing machines that learn to make decisions from data, thus going beyond the need of manually coding the decision rules.
As a matter of fact, coding rules is not always straightforward and it does not scale up very well. In many cases it turns out to be easier to collect examples, provide them to the machine, which is supposed to learn them automatically. Nowadays, it is clear that Machine Learning-based solutions are everywhere: We unlock our phone by letting it recognize our face, we use vocal interfaces to interact with our televisions, we receive recommendations of products that might be of interest to us, our cars can automatically recognize pedestrians, while we might ignore the role of machine learning in many other real-world applications.
The emphasis of this course will be on Neural Networks, where a number of units, called neurons, are interconnected to define the structure of a mathematical model that learns from data. We will discuss Deep Neural Networks, where large models with several computational layers are exploited, introducing their application to Vision and Language. The course will start with the mathematical tools that are needed to understand the basics of Machine Learning models, and it will finish with some practical code examples based on the TensorFlow platform.
Artificial Intelligence Machine Learning and Deep Learning course is organized into 6 modules.
Basic notions of linear algebra; basic knowledge of the Python language. A gentle introduction to mathematical topics relevant for the discipline is given to facilitate the access to the course.
The learner can take an exam at the end of each course.