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.
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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)
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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.
| Semester (Sem) | | 1 | First Semester | | 2 | Second Semester | | A | Annual course | Educational activities | | B | Identifying activities | Language |  | Course completely offered in italian |  | Course completely offered in english | | -- | Not available | Innovative teaching |  | 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)
- Soft Skills
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Academic Year
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2020/2021
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School
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School of Industrial and Information Engineering
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Name
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(Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
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Track
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T2A - COMPUTER SCIENCE AND ENGINEERING
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Programme Year
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1
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ID Code
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054307
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Course Title
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ARTIFICIAL NEURAL NETWORKS AND DEEP LEARNING
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Course Type
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Mono-Disciplinary Course
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Credits (CFU / ECTS)
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5.0
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Semester
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First Semester
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Course Description
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Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully solved many complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., training (neural) networks to simultaneously learn an optimal data representation and a model solving the requested task, has further boosted neural networks research and development. These models nowadays achieve human-like performance in natural language processing, text modeling, gene expression modeling, and image recognition, to name a few examples. This course provides a broad introduction to artificial neural networks (ANN), starting from the traditional feedforward (FFNN) and recurrent (RNN) neural networks architectures, till the most successful deep-learning models including convolutional neural networks (CNN) and long short-term memories (LSTM). The course aims at providing students with a theoretical background and the practical skills to understand and use ANN and, at the same time, become familiar and with Deep Learning for solving complex engineering problems.
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Scientific-Disciplinary Sector (SSD)
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Educational activities
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SSD Code
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SSD Description
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CFU
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B
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ING-INF/05
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INFORMATION PROCESSING SYSTEMS
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5.0
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| Schedule, add and remove | Alphabetical group | Lecturer(s) | Language | Teaching Assignment Details |
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| From (included) | To (excluded) |
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| --- | A | ZZZZ | Matteucci Matteo | |  |
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Details 
Class schedule planning