|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
The goal of the Machine Learning module is both to provide the basics of machine learning and pattern recognition theory and to expose the student to machine learning methods, workflows, and best practices, with emphasis on applications in Robotics and a focus on artificial neural networks as well as several other techniques.
The goal of the class is to present Artificial Neural Networks and other well-known Machine Learning techniques as systems for solving supervised and unsupervised learning problems, with a specific emphasis on Robotics applications. Such learning systems can be applied to pattern recognition, function approximation, time-series prediction and clustering problems. Some mention will be made to the use of ANNs as static systems for information coding, and dynamical systems for optimization and identification.
After successfully attending this course, students will have an exposure to many topics that underlie the field of machine learning, so that they will be able to autonomously apply the methods presented as well as other methods to concrete problems. During practical activities, students will both implement several methods from scratch, and use existing machine learning libraries, thus gaining a hands-on experience backed up by the theoretical concepts.
Assignments are used for continuous assessment whose weight is 50% of the final marks, the rest being obtained with a final exam and discussion.
Due to the teaching style and to the continuous assessment, attendance is mandatory
Course slides and assignments are available on the official study portal.
A selection of suggested readings (journal articles and textbooks) will be provided during lectures.
Office hours: All lecture days after class (approx. 20 min). Upon prior agreement, at any other time.
STEFANO ROVETTA (President)
RENATO UGO RAFFAELE ZACCARIA
ARMANDO TACCHELLA (President Substitute)
All class schedules are posted on the EasyAcademy portal.
The final exam consists in an interview with technical questions and exercises, and in the discussion of the assignments. Final marks given 50% by continuous assessment and 50% by exam.
About 30 hours of lectures and 18 hours of assignments / guided exercises.