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
(Bachelor of Science degree)(ord. 270) - MI (358) Engineering of Computing Systems
IT1 - Ingegneria Informatica e Comunicazioni
ALGORITHMS AND PRINCIPLES OF COMPUTER SCIENCE
Credits (CFU / ECTS)
Computer science models. Automata, grammars, Petri Nets, applications of mathematical logic to system modeling. Theory of computation. Church`s thesis; unsolvable problems. Computational complexity. Computational models and relations between their computational complexities. Linear acceleration. Algorithms and data structures. Order of magnitude of computational complexity; searching and sorting; fundamental data structures: stacks, queues, trees, hash tables. Backtracking, Divide et impera. NP-completeness.