With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
The sidebar on the left displays the number of lessons included in schedule. There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching. These CFUs include:
Subject taught jointly with companies or organizations
Blended Learning & Flipped Classroom
Massive Open Online Courses (MOOC)
School of Industrial and Information Engineering
(Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
T2A - COMPUTER SCIENCE AND ENGINEERING
COMPUTER ARCHITECTURES FOR DEEP NEURAL NETWORKS
Credits (CFU / ECTS)
Recent trends in Deep Neural Networks imposed hardware accelerators as the most viable solution for several classes of applications such as image classification for self-driving cars, computer vision and speech recognition. The goal of the course is to present the main optimization methodologies used in computer architectures to accelerate DNNs showing also commercial and research solutions.