|SCIENTIFIC DISCIPLINARY SECTOR||ING-INF/01|
The course aims at introducing the learner to the theory and applications of machine learning, particularly deep learning.
The course aims at providing the learner with state-of-the-art knowledge, both in terms of algorithms/models and tools, to tackle problems using machine learning techniques.
The main objective of the course is for the student to come to possess a broad knowledge of state-of-the-art deep learning techniques (dense, convolutional, recurrent networks). For each topic covered, the student will have the opportunity to learn the theoretical foundations, and to study some application examples. Exercises are proposed, and usually solved in class, for each topic in order to stimulate application and test knowledge acquisition. The examples and exercises in the course will use the python language and the Keras/Tensorflow library.
The learning outcomes relate to the realization of the above learning objectives, including through the analysis of application cases.
Fundamentals of programming (particularly python).
A series of seminars on programming will be offered initially so that everyone can take the course on a regular basis
Lectures face-to-face, using slides, and examples/exercises carried out on the PC (or in tele-learning, if made necessary), mainly using the Keras/Tensorflow library, in python language. Student Reception. Proposal, implementation and discussion of a project.
A. Geron, Hands-On Machine Learning With Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent, O’ Reilly
I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, The MIT Press
Lecture notes and other material suggested by the lecturer during the course
Office hours: On appointment: mail (firstname.lastname@example.org) or on Teams or after lecture
FRANCESCO BELLOTTI (President)
ALBERTO CABRI (President Substitute)
All class schedules are posted on the EasyAcademy portal.
Written and/or oral examination on topics covered in class
Verification of the knowledge acquired and the ability to apply it in contexts other than those presented in class will be assessed through questions in the interview or written examination.
The evaluation will also take into account the student's participation during the course.