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ARTIFICIAL INTELLIGENCE

The program provides in-depth knowledge in the field of artificial intelligence, both at the algorithmic and technological levels, which enables the construction of intelligent artificial systems, enhances the interaction between users and these systems, and supports the development of autonomous cognitive applications based on intelligent agents, natural language interfaces, computer vision, and machine learning.

Knowledge Representation and Reasoning

The course offers in-depth knowledge on different models of problem solving based on Artificial Intelligence techniques that are centered on both symbolic and non-symbolic representations of the problem domain. It is designed to offer students the understanding of different knowledge representation models and methods and their use in problem solving.

Syllabus

Data Mining

This discipline is studied within the field of data mining. It aims to familiarize students with the main
approaches, models, and explanatory theories of the field, used in solving practical applications and problems
relevant to stimulating the learning process in students.
The discipline addresses specific concepts and principles for data mining, that will contribute to
forming an overview of the methodological and procedural references related to the field to the students. The
following aspects are presented to students: association rules and sequential patterns, supervised learning,
unsupervised learning, semi supervised learning, Web usage mining, data warehousing and dimensional
modeling.
Syllabus

Computer Vision

Many methods and models from Computer Vision enable today’s computers to automatically interpret and understand images and video sequences for various applications. For example, today’s computers are able to detect and recognize human faces with almost perfect accuracy. They can also reconize human actions from video and object categories. The list of working computer vision applications is increasing at an exponential rate and the field is starting to mature, with a visible impact on industry and human life. Driveless cars, strongly relying on computer vision methods, have been developed (Google, Mercedes etc); computer games recognizing human actions are already available and best selling on the market (Microsoft Kinect); programs that automatically detect and recognize human faces are available on virtually every photo camera and image processing software. During this class we will start exploring the world of computer vision and discuss both theoretical as well as practical basic aspects of this field. By solving homework assignments and participating to laboratory classes students will acquire a solid hands- on experience on current computer vision problems, applications and methods.

Multi-agent Systems

This course provides an in-depth exploration of intelligent agents and multi-agent systems, focusing on their architectures, coordination, and problem-solving methods within distributed artificial intelligence. Students will learn how intelligent agents interact, cooperate, and negotiate to achieve goals in complex environments.

Natural Language Processing

This course has as objective to empower students to acquire methods and techniques of modelling, design, implementation, and evaluation of Natural Language Processing systems (NLP). This topic is probably the most important discipline within the field of Artificial Intelligence because natural language is what distinguishes humans from all other living beings. Students will be familiarized with the main, both state-of-the-art (based on deep neural networks) and classical (grammar and knowledge-based) approaches, models, and theories of the field. During the semester, students will also develop a project in teams, in which they will implement a NLP application.

Symbolic and Statistical Learning

Introductive elements of machine learning, statistics, information theory and decision theory. Linear models for regressions. Linear models for classifications. Kernel methods and Gaussian processes. Sparse kernel methods (Support vector machines and Relevance vector machines). Bayesian methods and graphical methods. Expectation maximization. Principal components analysis and Independent component analysis. Hidden Markov models.

Self-organizing Systems

An introduction to self-organizing systems. Bio-inspired self-organizing systems. Self organizing systems used in economy. Ant Colony Optimization. The social organism. Elements of Evolutionary Computation. Elements of social psychology. Culture in theory and practice. Thinking as a social process. Particle Swarm. Particle Swarm Optimization.

Scientific research

The student will develop a research project during the first year of the Master programme under the supervision of a coordinating professor that is part of the Master programme. The topic selected can be continued during the second semester and for the Master Thesis or can be changed in the second year. Students are encouraged to keep working on the same research topic all throughout the Master programme.

PARALLEL AND DISTRIBUTED COMPUTER SYSTEMS

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ADVANCED CYBERSECURITY

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FINANCIAL COMPUTING

QUANTUM COMPUTING

Basics of Linear Algebra for Quantum

Linear Algebra, the language of matrices and vectors, is the fundamental language of Quantum Computing and Quantum Information Theory. Approaching Quantum Computing through Linear Algebra is the strategy taken in the most cited textbook on the subject, namely Quantum Computation and Quantum Information by M. A. Nielsen & I. L. Chuang. This approach is also mathematically the easiest for the complete beginner.

• The principal aim of the course is to introduce students to the basics of Linear Algebra, with emphasis on the concepts required in Quantum Computing in particular and in Computer Science and Engineering in general.
• A second aim of the course is to develop students skills to approach and solve scientific problems of computer science nature, by broadening their horizon and achieving the ability to reason, given by the deepening of mathematics, at a certain stage of development of the individual.

Basics of Physics for Quantum

The goal of the course is to provide a broad understanding of the principles that govern the universe, with an emphasis on providing a quick ramp towards an introductory study of quantum mechanics. The course will be aimed at building a solid foundation for understanding the phenomena used in the present-day quantum computers and preparing the students for tackling the changes in this fast-evolving field.

Technical Scientific Writing

The ability of students to document and present their work in a meaningful and convincing way is one of the top three skills that IT recruiters currently list as requiring improvement in the training offered by the Computer Science Department to its students. The Technical and Scientific Writing lecture is designed to offer students the possibility to develop and improve these skills, using interactive techniques and practical hands-on activities. The lecture will offer students the opportunity to improve their writing and presentation skills on technical subjects. At the end of the lecture, students will be able to design, implement and deliver a technical presentation, to elaborate a scientific paper, a state-of-the-art survey, a scientific or commercial poster, and will also be able to draft the essential points of a research project proposal. Finally, the students will learn how to formally evaluate and review scientific papers, posters, and project research proposals.

Introduction to Quantum Computing

The study of the fundamental theoretical concepts and practical aspects related to quantum computing. From the qubit to quantum parallelism through no-cloning, entanglement, teleportation, superposition, etc.

Communication Networks

Understanding the methods to ensure a communication protocol between entities. Understanding how current networks operate (OSI/TCP-IP stacks). Configuring and managing networks. Managing services. Knowledge of some minimal security rules to prevent and counteract attacks on networks.

Topics in Cryptography

The overall objective of this course is to understand the fundamental primitives used in modern cryptography and to grasp the mechanisms behind popular security protocols, which are highly relevant in the context of quantum computing, such as one-way functions, RSA, Diffie-Hellman, and post-quantum algorithms.

Quantum Information Theory

Information theory represents the scientific study of quantifying, storing, and communicating digital information—elements upon which modern communications, cryptography, and decision algorithms have been built. In the quantum realm, quantum information theory, the language of mixed states and arbitrary quantum channels, provides many fundamental results that dictate what can and cannot be achieved with a quantum computer. This offers a mathematical foundation for causality as a physical principle, which in turn determines optimal compression rates and the minimum error rate for probabilistic communications and algorithms. Considering that all quantum states decohere and become mixed, quantum information theory is crucial for all real-world applications in Quantum Computing. The primary aim of the course is to familiarize students with the basic elements of Information Theory, both classical and quantum, with an emphasis on the concepts necessary in Quantum Computing and, in particular, quantum communications, as well as in Computer Science and Engineering in general. A secondary goal of the course is to help students develop problem-solving skills in the field of computer science by building a mathematical background and fostering abstract thinking through exposure to selection, extension, and the exploitation of quantitative metrics in order to obtain correct results from imperfect practical data.

Quantum Communications & Cryptography

Shor’s algorithm highlighted critical errors in secure communication using classical cryptography, one of the solutions being Quantum Communications and Quantum Cryptography. Given the current development of terrestrial and satellite quantum communications, as well as the advancement of quantum computers, Quantum Communications and Quantum Cryptography are becoming extremely important for the real-world applications of Quantum Computing. They provide fundamental primitives such as routing, error correction, key distribution, and distributed computing, which lead to applications like the Quantum Internet and quantum blockchains, as well as theoretical results regarding the classical simulation of quantum operations, such as the Gottesman-Knill theorem, which establishes “magic state factories” as key components of any quantum computing architecture.

The primary objective of the course is to familiarize students with the basics of quantum communications and cryptography, with an emphasis on the strong impact of experimentally tested extensions of certain concepts from classical Computer Science and Engineering in general. A secondary objective of the course is for students to develop skills in approaching and solving scientific problems in the field of classical computer science by applying their classical background to solve real problems in the quantum realm, such as routing, distribution, and distillation, as well as key distribution protocols.

Parallel & Distributed Systems

Understanding methods to ensure the security of stored or in-transit information when using shared physical resources (storage and networking). Knowledge of the systems that establish the identity of entities and the trust relationships between them. Familiarity with minimal security rules to prevent and counteract attacks in the virtual environment.

Machine Learning

Obiectivul acestui curs este sa ofere o introducere in teoria, metodele, algoritmii si aplicatiile agentilor inteligenti si ale sistemelor multi-agent. Acest curs contine metode si algoritmi atat pentru agenti unici (cum sunt agentii MDP, agentii POMDP, agentii RL) si sistemele multi-agent, si ii va ilustra in contextul diferitelor domenii de aplicatii. Studentii sa dobandeasca cunostinte teoretice si practice despre agenti inteligenti si despre sistemele multi-agent. Studiul diferitelor tipuri de agenti si de sisteme multi-agent. Invatarea metodelor de rationare utilizate de agentii inteligenti. Invatarea de metode de dezvoltare a aplicatiilor bazate de paradigma multi-agent. Dezvoltarea de aplicatii bazate pe agenti inteligenti. Abilitatea de a proiecta si de a dezvolta sisteme multi-agent.

Quantum Algorithms

Quantum Algorithms are algorithms which run on a realistic model of quantum computation, chiefly the quantum circuit model. Whereas a classical algorithm is a finite sequence of instructions solving a family of problems, quantum algorithms can also use a finite sequence of quantum gates forming quantum circuits. In the real world, Quantum Algorithms, provides many remarkable results which dictate what problems can and cannot be practically solved with a quantum computer, such as the efficient factoring of large numbers, leading to Quantum Algorithms often being characterized as one of the main highlights in the advent of Quantum Information Processing, alongside Quantum Communication. Given the fact that all physical quantum computers are built to more efficiently solve real-world problems, Quantum Algorithms are crucial to all real-world applications of Quantum Computing.

• The principal aim of the course is to introduce students to the most famous Quantum Algorithms and their applications to classical Computer Science problems in particular, and to real-world engineering problems in general.
• A second aim of the course is to develop students skills to approach and solve scientific and engineering problems of computer science nature, by developing their ability to reason about abstract concepts through development of algorithmic background and implement abstract reasoning to real world problems through exposure to the methods by which algorithms are developed to take advantage of quantum phenomena to drive lower complexities, deriving correct insightful results from practical noisy data.

Quantum Physical Realizations

Quantum Physical Realizations are a key aspect of quantum computing and quantum communication as they offer the technological foundation for practical implementation and adoption in real-world applications. Quantum Physical Realizations provide insights into how well protocols actually work in experimental and industrial conditions, and how their key build blocks: gates and measurements, have been physically implemented in the state of the art, leveraging various types of quantum systems that exhibit quantum phenomena. Given the fact that the ultimate test for any protocol is if it physically reproducible, Quantum Physical Realizations is crucial to real-world application of Quantum Computing.

• The principal aim of the course is show students how the concepts they have learned throughout the first year are implemented in recent papers, ensuring they have an understanding of the novel technologies that implement quantum information processing.
• A second aim of the course is to develop students’ abilities to stay up to date with and to present new scientific and engineering results, by exposure to recent top-quality scientific papers on various subjects across Quantum Computing, combining the familiar concepts that they know with the unfamiliar novel technologies presented in each paper.

SERVICE ENGINEERING AND MANAGEMENT

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CYBER-PHYSICAL SYSTEMS

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ROBOTICS AND AUTOMATION

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Advanced Analytics for Business

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