Educational objectives General objectives:
The course aims at training professionals able to successfully face the challenges posed by the security problems of the information society.
Specific objectives:
The course includes the study of various models of access control, analysis of the main difficulties and resolutions of cryptographic problems and the main security protocols used in the network
Knowledge and understanding:
Upon passing the exam, the student will have knowledge and understanding of the bases of computer security and of the main technologies for the analysis and solution of security problems.
Apply knowledge and understanding:
The course enables students to apply their knowledge and understanding skills to solve IT security problems, with sufficient autonomy to deal with complex problems; and for the effective consultation of advanced scientific and technological documentation.
Autonomy of judgment:
The course aims to acquire autonomous interpretation skills to propose solutions to security problems congruent with the available technologies, and to continuously update the technological evolution, to formulate independent critical judgments contributing to the progress of system security.
Communication skills:
Students acquire the ability to present and to argue their ideas about the security problems faced and the solutions proposed, both with colleagues and with users
Next learning ability:
The course provides for the development of in-depth capabilities in the field of computer security both of methodological and technological aspects, to adapt to the progress of techniques and solutions to the most common security problems, and to continue autonomously to solve new problems. safety issues.
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Educational objectives General goals:
Familiarity with advanced machine learning techniques, both supervised and unsupervised; modeling skills of complex problems using deep learning techniques, and their application to diverse applicative settings.
Specific goals:
Topics include: deep neural networks, their training and the interpretation of results; convolutional networks and prominent architectures; theory of deep learning and convergence; programming frameworks for implementing advanced machine learning techniques; autoencoders; adversarial attacks.
Knowledge and understanding:
How neural networks work and their mathematical interpretation as universal approximators. Understanding the limits and potentials of advanced machine learning models.
Applying knowledge and understanding:
Design, implementation, deployment and analysis of deep learning architectures addressing complex problems in several applicative areas.
Critical and judgmental abilities:
To be able to evaluate the performance of different architectures, and to assess their generalization capabilities.
Communication skills:
To be able to communicate clearly how to formulate an advanced machine learning problem as well as its implementation, its applicability in realistic settings, and specific architectural and regularization choices.
Ability to learn:
Understanding alternative and more complex techniques such as generative models based on optimal transportation, scattering transforms and the energetic profile of neural networks. To be able to implement existing techniques efficiently, robustly and reliably.
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Educational objectives General objectives:
At the end of the course the students know the theories, models and rules that guide the project and the development and validation of usable interfaces and interactive systems.
Students who pass the exam are able to design interactive systems following the criteria of human-computer interaction, analyzing the user's role, the scenarios and the main tasks, and taking into account the implementation constraints through project cycles and development very short.
Specific objectives:
Knowledge and understanding:
At the end of the course the students know the theories, the models and the rules that guide the project of interfaces and usable interactive systems.They also know the principles of agile design centered on the user.
Apply knowledge and understanding:
Students apply the knowledge gained in designing an interface as a group work for the exam.
Critical and judgmental skills:
Students, also through practical exercises, acquire skills in the evaluation and validation of human computer interfaces and develop judgment on the usability of an interface and therefore on the effects of the use of the interface in terms of effectiveness, efficiency and satisfaction. .
Communication skills:
The students support two presentations of their group work during the two revisions scheduled with the teacher. The first review is carried out in the classroom and the presentation is therefore aimed at all colleagues in order to exercise communication skills.
Learning ability:
The learning capacity is stimulated through 1) guided and autonomous supervised planning activities; 2) exposure to realistic design problems by stimulating the independent search for non-standard solutions; 3) the presentation of real cases and stimulating their critical discussion.
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Educational objectives General goals:
Introduction to mathematical modelization of optimization problems, Linear and Integer programming and their applications in real contexts.
Specific goals:
To learn:
1. Main problem modelling techniques using mathematical and logical language
2. Main theory properties and their practical applications for optimization problems
3. Linear programming and simplex algorithm and its applications.
4. Competences for software for modelling and optimization
Knowledge and understanding:
develop
1. the ability of conciseness, of logical reasoning and problem solving trhough quantitative models;
2. the ability to describe and solve e risolvere (also at high level) computationally hard problems;
3. the ability to use english written bibliography and software;
4. the ability to identify precisely optimization problems and when they are linear integer or non-linear;
5. the ability to design a implementable version of mathematical optimization problem model and finding solution
for the model using appropriate algorithms. Interpreting the solutions.
Applying knowledge and understanding:
1. Real applications of optimization problems (especially on networks)
2. Skills on using software for modelling and optimization
Critical and judgmental skills:
Enabling autonomous thinking in the student by deepening the ability of mathematical reasoning of the student through the development of logical language and problem solving abilities.
Communication skills:
Group work aimed at solving optimization problems drawn from everyday cases, develop communication abilities in explaining and focusing optimization problems on concrete working examples
Learning ability:
Skills and theoretical tools acquired during the course are basic for more advanced courses on topics concerning computational complexity, network algorithms, graph theory.
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Educational objectives General goals:
The aim of the course is the study of java language support to server side programming, for the realization of web based applications. The course will also provide a critical comparative analysis of several approaches to realize the same functionalities, for a series of problems common in the development of web applications.
Specific goals:
Server Side programming through Java Servlet and JSP.
Knowledge and understanding:
Through this course, students will comprehend how the java language supports the realization of web applications. In particular, students will focus on the motivation at the basis of all the implementation choices with reference to the client server –architecture and network protocols in use.
Applying knowledge and understanding:
Through this course, students will develop the capability to determine among potential solutions which is the most suitable in terms of performance, security, portability and efficiency.
Critical and judgmental abilities:
The course will provide students with sufficient tools and methodologies to perform a comparative analysis of different potential solution methodologies.
Communication skills:
Students will be able to motivate the solutions adopted to design a specific web application, and to provide a comparative analysis of the chosen solutions with respect to other potential approaches.
Learning ability:
Students will develop the capability to autonomously study and search for new solutions and to evaluate new methodologies, technologies and models for the development of Web applications.
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