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 | 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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2018/2019
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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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2
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ID Code
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055126
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Course Title
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ADVANCES IN DEEP LEARNING WITH APPLICATIONS IN TEXT AND IMAGE PROCESSING
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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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Annual course
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Course Description
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Nowadays deep learning spans multiple fields in science and engineering, from autonomous driving to human machine interaction. Deep networks have outperformed traditional hand-crafted algorithms, and achieved human performance in solving many complex tasks, such as natural language processing and image recognition. A plethora of papers presenting the success of deep learning in different scenarios is steadily being published, and most often papers frame on a few, very successful, architectures. These architectures are nowadays becoming de facto standards in deep learning such as: convolutional neural networks (CNN), long-short time memories (LSTM), generative adversarial networks (GAN), graph networks, to name a few examples.
Our goal is to provide the students with the skills to understand, become familiar, and use for their research the most successful architectural patterns in deep neural networks.
This is intended as an advanced course, thus proficiency in neural networks and basic notions of non linear optimization and image/signal processing are assumed as pre-requirement to the participants.
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| Schedule, add and remove | Alphabetical group | Professor | Language | Course details |
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| From (included) | To (excluded) |
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| -- | -- | -- | Docente non definito | | -- |
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Details 
Class schedule planning