Educational objectives General Objectives:
The course aims to provide an overview of corporate investment and financing choices and an assessment of the trade-offs associated with such decisions. More specifically, the course aims to provide students with tools and knowledge useful in understanding and analyzing corporate and shareholder financial objectives, investment evaluation, capital management, financial statements, and budgeting.
Specific objectives:
Knowledge and understanding:
To learn basic concepts related to corporate investment and financing decisions, the separation of ownership and control, and the objectives of corporate governance. To learn basic accounting principles: balance sheet and income statement formation, civil accounting balance sheet and reclassification, accounting systems and accounting methods, revenues and monetary assets, inventories and cost of sales, fixed assets and depreciation, liabilities and equity, and financial statement analysis. To learn basic concepts of analytical accounting: direct and indirect costs, budgeting, and management control.
Apply knowledge and understanding:
To be able to apply methodologies for investment analysis, to read and analyze a financial statement, to create a sales, production, and cash budget.
Making judgements:
To be able to interpret corporate financial data, including indicators of profitability, liquidity, solvency, and operational efficiency. To develop skills for assessing the financial health of companies, identifying strengths and weaknesses through balance sheet analysis.
Communication skills:
The course involves presentations by guest speakers who present case studies or in-depth analyses of specific topics. These seminars allow students to interact with industry professionals or company stakeholders, enhancing their communication skills with people outside the academic environment.
Learning skills:
The fundamental notions acquired in the course enhance the student’s learning abilities, allowing them to further explore topics in business economics with a certain degree of autonomy.
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Educational objectives General objectives.
The course focuses on concepts of operating systems and computer networks, as well as their structure and mechanisms. It analyzes an operating system's elements and the network protocol stack levels.
Specific objectives.
Study of the elements of an operating system. Concurrency, mutual exclusion and deadlock. Semaphores. Process scheduling. Communication mechanisms. Introduction to the security of operating systems and computer networks. Distributed applications that use Internet network services, both following the client-server and peer-to-peer paradigms. In-depth analysis of the TCP/IP protocol stack with reference to the application level, transport and network layer evolutions, software-defined networking, quality of service support, protocols and techniques for creating multimedia applications on the Internet and wireless access.
Implementation of projects using the tools that operating systems and computer networks offer in order to allow cooperation and concurrency between processes and threads.
Socket programming. Development of distributed applications using the services offered by the Internet.
Knowledge and understanding:
Knowledge of the elements of the operating system and computer networks. In-depth understanding of the motivations behind the design choices adopted in creating the protocol stack. Knowledge of the design of networked systems. Understanding multiprogramming and the problems inherent in the communication and synchronization of multiple processes or threads, even on different devices.
Apply knowledge and understanding:
Knowledge of creating first distributed applications on the network, using socket programming. Knowledge of network protocol implementation. Developing reliable and efficient multi-process and multi-threaded applications through the use of system calls that the operating system makes available and thread libraries.
Critical and judgment skills:
Understanding the mutual exclusion and deadlock problems that an algorithm can have. Be able to understand the most appropriate implementation solution depending on the specific requests. Understanding how to design a protocol stack or how to configure it according to the different quality of service needs of the application to be created. Designing and creating distributed applications on the network, defining their functionality, selecting the application paradigm, the most suitable transport level, and the best techniques for implementing the application.
Communication skills:
Describe the elements of the operating system and a protocol stack and communicate the choices made in developing concurrent applications and network protocols and applications.
Learning ability:
Using the knowledge acquired in advanced courses on operating systems, computer networks, distributed systems and cybersecurity courses. Ability to read and understand industry standards.
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Educational objectives General Objectives
The general aim of the course is to introduce the theoretical foundations of computer science, particularly focusing on the basics of computation theory and complexity theory, and their implications on two fundamental aspects of a computer engineer's preparation: mathematical logic and algorithms. Mathematical logic will be introduced as a powerful tool for modeling and formally reasoning on different aspects of information technology, in particular data management, database querying, program specification and reasoning on the properties of programs and automata. Additionally, the course will cover basic notions related to analysis and design of probabilistic algorithms, algorithms used in dynamic optimization and methods and techniques for classification and machine learning.
Specific Objectives
Knowledge and Understanding:
Students will grasp fundamental concepts of computation models and computational complexity, as well as mathematical logic, the principles according to which the validity of arguments is judged, the relationships between arguments are analyzed and inferences such as deduction, induction and abduction, are evaluated. Students will also learn the basic notions of probabilistic methods and dynamic optimization and acquire the basics to apply these notions to the analysis and design of fundamental algorithms in computer science, including sorting algorithms, algorithms on networks and graphs, classification algorithms, clustering and machine learning.
Applying Knowledge and Understanding:
Students will gain deep understanding of problem decidability/undecidability and tractability/intractability, and of the role of logic in various aspects of a computer engineer's activities. They will acquire basic knowledge to formalize a problem in logic, analyze logical theories and reason about their inferences, construct logical theories for modeling requirements of a moderately complex information system, specify database queries in logic, specify properties of automata in logic, and translate simple computational specifications into logic programs. Additionally, students will gain a deep understanding of the role of the analysis and design of algorithms in various aspects of the activities of a computer engineer and acquire basic knowledge to carry out analysis of probabilistic algorithms, define probabilistic algorithms for problems of medium complexity, apply fundamental methods such as the Monte Carlo method, Markov chains, dynamic programming and Bayesian models to different contexts, such as sequences, graphs, networks, machine learning, classification and clustering.
Critical and judgment skills:
At the end of the course, students are able to evaluate the validity of statements and arguments, the consistency of a set of requirements for an information system, the adequacy of the formulation of a computation that extracts data from a database, the correctness of a program with respect to the specification of certain properties. Students are also able to establish the decidability/complexity of a problem, to analyze probabilistic algorithms, to evaluate the effectiveness of probabilistic and dynamic optimization methods for algorithmic problems and to judge the quality of the application of machine learning, classification and clustering algorithms.
Communication skills:
The design activities and exercises of the course allow the student to acquire crucial tools to communicate and share the critical evaluation of logical tools and languages and their role in different fields of computer science and algorithmic methods and their role in different important contexts of computer engineering.
Learning ability:
In addition to the classic learning skills provided by the theoretical study of the basic topics covered in the course, the methods of carrying out the course itself, in particular the design activities, stimulate the student to autonomously study some topics, to work in groups and concrete application of the concepts and techniques learned during the course.
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Educational objectives The course aims to provide a basic knowledge of an electronic system as
system for data elaboration focusing on gain for the different types of
amplifiers and on the limitations due to band width, power dissipation
and noise for both analog and digital circuits.Risultati di apprendimento attesi
(Inglese):
The student will be able to
analize simple electronic systems identifying the behavior with and
without capacitive elements in both analog and digital circuits
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Educational objectives the final exam consists of the presentation of an essay related to the activities conducted during the stage/Thesis-Work.
The preparation for this exam make it necessary for the student to get skills related to the presentation of her/his work,and the capability to discuss and argue with an audience fully aware of the topics presented.
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