Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Computer Engineering for intelligent Systems - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year It will be activated in the A.Y. 2025/2026
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3 modules among the following| Modules | Credits | TAF | SSD |
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3 modules among the following| Modules | Credits | TAF | SSD |
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4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics
- 2nd year: Advanced control systemsLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Data management and machine intelligence (2024/2025)
Teaching code
4S012352
Credits
12
Coordinator
Language
English
Also offered in courses:
- Deep Learning of the course Master's degree in Computer Engineering for Robotics and Smart Industry
- Advanced database & information systems of the course Master's degree in Computer Engineering for Robotics and Smart Industry
Courses Single
Authorized
The teaching is organized as follows:
Learning objectives
The course is composed by two modules. The objective of the first, data management systems, is to allow students to acquire in-depth knowledge of the methodologies and tools necessary to manage large amounts of data in new systems not based on the relational model (we will therefore consider systems based on semi-structured or document-based models, NoSQL and extended models with time and space dimensions). In particular, the systems that must memorize data also produced by sensors and mobile devices will be considered, so that a correct integration of these new data sources with the corporate information system is possible. At the end of the course, the student will be able to design and query non-traditional databases with typical tools of the NoSQL approach. The objective of the second, "deep learning",is to provide the fundamentals of neural networks as evolution of linear models, including architecture, activation functions and backpropagation. He will know the basics of the optimization algorithms used in the training of neural networks. Furthermore, it will provide knowledge on basic neural network architectures: the student will know various types of neural networks such as convolutional neural networks (CNN) for image analysis, recurrent neural networks (RNN) for sequential data and transformers for natural language processing. He/she will be able to understand the design principles behind these architectures and their applications, and the underlying mathematical theories. He/she will also be able to apply the techniques to real-world problems, understanding what data inputs and outputs should be. The course will present also methods for data preprocessing and augmentation. In the second part the course will also present advanced neural network architectures, used to tackle natural language processing (NLP) and computer vision (CV) problems such as text classification, object recognition and machine translation. In this regard, the student will know how to use advanced tools such as variational encoders, generative adversarial networks, NERF, and large language models. Interpretability: The student will learn the basics of interpretability of a neural network. This will make it possible to offer guarantee tools on the results produced by neural networks. Deep Learning Frameworks and Tools: The student will have gained hands-on experience with popular deep learning frameworks such as TensorFlow, PyTorch, and Keras. You will be able to build, train, and evaluate deep learning models using these frameworks.
Prerequisites and basic notions
Data Management Module: Fundamentals of computer science (programming logic, basic data structures), operating systems (file management, process and thread concepts), basic mathematics (algebra, logic). TCP/IP and network models.
Deep Learning Module: Linear Algebra (Vectors and matrices, matrix operations, eigenvalues and eigenvectors, vector spaces and their transformations), Calculus (derivatives and integrals, partial derivatives), Probability and statistics, Programming (Python or Matlab), Machine learning (supervised and unsupervised models)
Bibliography
Criteria for the composition of the final grade
The final grade is assigned as the sum of the grades of the two modules, which will range from 9 (pass) to 15 (the maximum for a course). Honors will be assigned by mutual agreement between the teachers of the two modules.
