Departamentul de Informatica
Facultatea de Stiinte
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Course Catalogue

Bachelor Degree in Computer Science

      1st YEAR

  •   1st SEMESTER
    • Calculus - 5 ECTS
      1. Introduction: Normed linear spaces, Metric spaces. Functions: real valued functions of a single real variable, vector valued functions of a single real variable, real functions of several real variables, vector valued functions of several real variables; direct image of a set. (3 hours)
      2. Sequences and series: Convergence of sequences in metric spaces: convergent sequence, Cauchy sequence. Real sequences: Bolzano Weierstrass theorem. Complete metric space. Sequences in Rp. Sequences of functions. Convergence of numerical series: series with positive terms, absolutely convergent series, alternated series, approximating the sum of a convergent series. Series of functions. Power series. (4 hours).
      3. Continuous functions: Limits of functions. Continuous functions and uniformly continuous functions. Curves. Linear and continuous operators. (3 hours)
      4. Differentiable functions: Differentiability of functions of a single real variable. Real valued functions of a single real variable:  the derivative at a point, higher order derivatives, Taylor formula, Mac-Laurin formula, extrema. Power series, representation of a common function as a power series. Vector valued functions of a single real variable: the derivative at a point, curves.   Differentiability of functions of several real variables: partial derivatives, gradient, jacobian matrix, higher order partial derivatives. Taylor formula, extrema. (8 hours)
      5. Riemann integral: definition, properties, the length of a curve. Improper integrals. (2 hours)
      6. Multiple integrals: calculation of a double integral and of a triple integral; change of variables in multiple integrals (polar coordinates, cylindrical coordinates, spherical coordinates) (3 hours).
      7. Line integrals: Line integral of a scalar field. Vector field; line integral of a vector field. Path independence. Green-Riemann formula. (3 hours)
    • Algebric Foundations of Computer Science - 5 ECTS
      1. Sets. Operations with sets. The Boolean ring; Binary relations on a set. General properties. Equivalence (congruence) relations. The factorization of a set (quotient set) by an equivalence (congruence) relation;
      2. Relations of (pre) order on a set. Total ordered sets (chains). Special elements in an ordered set (bottom,top, minimal, maximal, irreducible, atom ,etc). Natural order relations on the main sets of numbers. Inductive sets. Zorn’s lemma;
      3. Semilattices. Lattices. Filters. Ideals. Morphisms of (semi) lattices; Modular lattices. Distributive lattices. The quotient lattice by filters or ideals;
      4. The complement of an element in a bounded distributive lattice. Boolean algebras. The connections between Boolean algebras and Boolean rings. Morphisms of Boole algebras; C5:The factorization of a Boolean algebra by filters. Ultrafilters in a Boolean algebra. Stone representation theorem.
      5. The factorization of a Boolean algebra by filters. Ultrafilters in a Boolean algebra.Stone representation theorem.
      6. Algebraically operations. Semigroups. Monoids. Morphisms of monoids. The monoids (N,+), (N, .). Group. Subgroup. Calculus in a groups. The subgroup generated by a set. Cyclic groups. The order of an element in a group. The lattice of subgroups of a group. The index of a subgroup in a group. Lagrange theorem.
      7. Normal subgroups. Factor group. Cauchy theorem for finite groups. Applications. Morphisms of groups. Isomorphisms of groups. The transport of subgroups by morphisms of groups. The group (Z,+). The Subgroups of the group (Z, +). The monoid (Z, .). Theorems of isomorphisms for groups. Direct product of groups. Chinese remainder theorem.
      8. Groups of permutations. Cayley theorem. Transpositions. Cycles. The decomposition of a permutation in a product of disjoint cycles.
      9. Ring.Calculus in a ring. Specials elements in a ring: zero divisors, unit elements, nilpotent elements. Subring. Subring generated by a set. The lattice of subrings of a ring. The finite ring of classes modulo n : (Zn, +, .). Integral domain. The integral domain (Z, +, .). Left (right, bilateral) ideal. Ideal generated by a set. Principal ideal. The lattice of ideals of a ring. Operations with ideals. Factorization of a ring by a bilateral ideal. Morphisms of rings. The transport of ideals and subrings by morphisms of rings. Theorems of isomorphisms for rings.
      10. Field. Subfield. Calculus in a field. Morphisms of fields. The characteristic of a field. The field (Q,+, .) of rationals numbers. The field of reals (R,+,.), complex (C, +, .) and quaternions (H, +, .).
      11. Rings of polynomials. Rings of polynomials in one variable with coeficients in a commutative unitary ring. Construction. Universal property. Rings of polynomials in many variables. Construction. Universal property. Symmetric polynomials. The fundamental theorem of symmetric polynomials. The fundamental theorem of algebra (D’Alembert – Gauss).
      12. Determinants and systems of linear equation.
      13. Vector spaces.
      14. Morphisms of vector spaces.
    • Logics and Computer Science - 6 ECTS
      1. Introduction
      2. Propositional Logic
      3. Propositional Equivalences
      4. Semantic Reasoning
      5. Natural Deduction
      6. Predicate logic
      7. Inference Rules
      8. Proofs - methods and strategies
    • Algorithms and Programming - 6 ECTS
      1. Steps to achieve a program
      2. Elementary algorithms
      3. Data types, constants and variables
      4. Instructions decision Loop, select
      5. Operators Increment / decrement, relational, logical, bitwise, conditioning, conversion, precedence and associativity
      6. Works Features
      7. Features Input / Output format conversion
      8. Recursion
      9. Preprocessor directives
      10. Structures, Unions
      11. Pointers, dynamic allocation
      12. Pointers to functions
      13. Command line arguments
      14. Pointers to structures
      15. Structures and functions
      16. Functions with variable number of parameters
      17. lists
      18. File management. Functions Input / output level. Operations at registration level
      19. Class variable allocation
    • Computer Architecture - 5 ECTS
      1. Data Representation in computing systems:Generations of computers,representation of numeric and nonnumeric information, corrective and detectors codes of errors
      2. Boolean algebra and logic digital:Boolean algebra, logical gates, digital components, combinational circuits,sequential circuits
      3. Classical von Neumann Architecture:Central Processing Unit, memories, Input-output units, peripheral equipments
      4. Alternative Architectures:RISK Machines,Flynn's taxonomy, parallel and multiprocessor architectures, approaches on parallel processing alternatives "
      5. Software System: Operating systems, protection equipments , programming tools, software for database, transactions management
      6. Computer networks organization and architecture: Academic and business environment of computer networks, network architectures. ISO-OSI protocols and TCP/IP, computer networks organization ,high digital capacity connections, internet Network
    • English for Computer Science (I) - 3 ECTS
    • Sports - 1 ECTS
  •   2nd SEMESTER
    • Data structures and Techniques for Developing Algorithms - 6 ECTS
      1. Introductory notions. The complexity of Algorithms.
      2. Algorithms. Recursion. Divide et Impera. Greedy. Backtracking. Dynamic programming.
      3. Data structures: single linked lists, double linked lists, stacks, queues
      4. Sorting methods: Selection Sort, Insertions Sort, Shell Sort, Bubble Sort, Merge Sort, QuickSort.
      5. Graphs. Trees.
      6. HashTable
    • Fundamental Algorithms in Artificial Intelligence - 5 ECTS
      1. Intelligent Agents:How Agents Should Act, structure of Intelligent Agents, properties of Intelligent Agents Environments
      2. Problem-Solving Agents: Formulating Problems, search Strategies, informed Search Methods
      3. Characteristics and Constraints:Constraint Satisfaction Problems, generate-and-test Algorithms , consistency Algorithms, local Search Algorithms
      4. Modern Methods for Solving Problems:Evolutionary Algorithms , artificial Ant Colony Optimization, artificial Immune Systems
      5. Game Playing:Minimax Algorithm, robocode – a Multi-agent Environment, reinforcement Learning
    • Object Oriented Programming - 6 ECTS
      1. Introduction to Object Oriented Programming:Basic concepts in object oriented programming,classes and Objects, access specifiers
      2. Creating and destroying objects:Constructors,types of constructors, destructors
      3. Dynamic memory allocation
      4. Static Data Members and Methods
      5. Friend functions and classes
      6. Operators overloading:Overloading operators using friend functions, overloading operators using member functions, overloading special operators
      7. Representing classes using UML diagrams
      8. Techniques for code reuse: Composition, inheritance, polymorphism - virtual functions and classes, template functions and classes
      9. Exception handling in C++ language
      10. I/O Operations. Streams
      11. Standard Template Library – STL
      12. Design Patterns
    • Operating Systems - 5 ECTS
      1. Structural description of a computing system. Hierarchical relationships in a computer system. Hardware as basic system generator. Information flow in computer systems. Functional description of the processor. Functional description of memory. Functional description of link units and of peripheral devices. Structure of operating systems. Concepts of operating systems. Operating systems models.
      2. Processes and threads. The processes model. Creating and terminating processes. Hierarchy of processes. The states of the processes. Processes implementation. Threads. Inter-processes communication. Race condition. Critical sections. Producer-consumer problem. Mutual exclusion via "Busy waiting". Sleep a¬¬nd Wakeup. Semaphores. Message passing. Classical problems of inter-processes communication.
      3. Processes scheduling. Round robin scheduling. Scheduling based on priorities. Scheduling with multiple queues. Deadlocks. Deadlock modelling. Deadlock detection and elimination.
      4. Memory management. Basic memory management. Swapping. Virtual memory. Memory paging. Page tables. Page replacement algorithms. LRU algorithm. FIFO algorithm. The second chance algorithm. Segmentation. Implementation of segmentation.
      5. Input/Output. Principles of I/O hardware: I/O devices, and controllers. Principles of I/O software: goals of I/O software; programmed I/O; I/O operation driven by interrupts; DMA.
      6. File systems. Naming, structure, types, access mode, and file attributes. File operations. Folders. Folders levels. Folders hierarchies. Path naming. Folders operations. Files implementation. Folders implementation..
    • Geometric Algorithms - 5 ECTS
      1. Elements of analytical and affine geometry. Geometric Transformations
      2. Basic Computational Geometry algorithms: Orientation of triangles, the problem of the road simply closed and the problem of belonging to the Interior of a polygon.
      3. Convex Hull problem
      4. Partition of a polygon into triangles. Algorithms for triangulation
      5. Segments intersections. The Manhattan geometry
      6. Voronoi diagrams and Delaunay Triangulation
      7. Shortest paths problem and the visibility graphs
    • English for Computer Science (II) - 3 ECTS
    • Sports - 1 ECTS

      2nd YEAR   

  •   1st SEMESTER
    • Graph Algorithms - 6 ECTS
      1. Algorithms and complexity:Algorithms and computable functions, The complexity of calculation, Asymptotic complexity, Applications of asymptotic notations.
      2. Finite undirected graphs:Representation methods, Connected graphs-Graph traversal, Hamiltonian and Eulerian cycles in an undirected graph.
      3. Tree data structures:Trees-Definition,Minimum spanning tree,Binary trees
      4. Directed graphs. Algorithms: Graphs representation and graphs traversal, Strongly connected graphs-Hamiltonian paths-Minimum distances-Matrix algorithms
      5. Network flows in transport networks:Maximum flow problems in transport networks, Minimum flow problems in transport networks, Matching and vertex cover in bipartite graphs-Perfect matching-Complete matching-Algorithms, Applications of graph theory to engineering and computer science.
    • Computer Networks - 6 ECTS
      1. Introduction. History, architecture, topology. Protocols stack. Levels design issues. Interfaces and services. The relationship between services and protocols. Standards.
      2. Reference models. ISO-OSI reference model. TCP/IP reference model. Comparison with ISO-OSI model
      3. Physical level. Shannon's Law. Particularities of various physical environments
      4. Data Link layer. Detecting and correcting errors. Flow control. Elementary Data Link protocols. MAC Sub-level. Ethernet network. Packet switching
      5. Switches. Virtual networks (VLAN)
      6. Network level. Routing algorithms. Congestion Control. IP protocol. IP addresses. IPv6 protocol. ICMP protocol
      7. Configuration via DHCP. ARP protocol
      8. Transport layer. Transport layer services. Communication primitives. Transport layer protocols
      9. Communication sockets.
      10. Application level. Domain Name System (DNS)
      11. Securing computer networks. Firewalls. Security models.
    • Java Technologies - 6 ECTS
      1. Introductory Elements: Oracle Java Technologies: Java Standard Edition, Java Enterprise Edition, Java Micro Edition; Java basics: Character Set, Keywords, reserved words, identifiers, literals, separators, comments, operators, variables, expressions, instructions, Vectors, Strings, Arguments
      2. Classes and Objects in Java: Objects and classes. Relations between classes. Relationships between objects; Creating Objects, Destroying Objects; Subclasses and inheritance, Variables "shadow", Overriding methods; Data Hiding and Encapsulation; Abstract classes. Interfaces
      3. Exceptions and their handling: Exception handling, Throwing Exceptions; Class hierarchy exceptions, Special Exceptions; Advantage of exceptions handling;
      4. Java Mini-applications programming (Applets): Basic concepts, restrictions, benefits; The structure of Java Applet; AWT graphics components; Handling events generated by AWT components; Java Applets: Tips & Tricks
      5. Graphical interfaces in Java: Graphical User Interfaces; Interfaces Development; Java Foundation Classes (JFC); The MVC (Model View Controller); Java Swing components and library packages
      6. Threads in Java: Java Thread state; Working with threads in Java: Extending Thread class, Runnable interface implementation; Threads Synchronization
      7. Java Database Connectivity-JDBC: JDBC Drivers; Accessing a database using JDBC; Example
      8. Java Servlets: Introduction; Working with servlets; Structure of Java Servlet; doGet() and doPost() methods; Example of implementation
      9. Java Micro Edition: Configuration, Profile; CLDC Configuration: CLDC virtual machine specification, CLDC class library; MIDP Profile: MIDP Java Applications; Java archive file - JAR (Java Archive); Application descriptor file - JAD (Java Application Descriptor); Developing MIDlets
    • Probabilities and Mathematic Statistics - 6 ECTS
      1. Combinatorial Analysis
      2. Sample space and Events. Axioms of Probability. Conditional Probability. Bayes’s Formula. Independent Events.
      3. Discrete Random Variables. Probability mass function. The cumulative distribution function. Expected value. Variance. The Bernoulli and binomial random variables.
      4. The Poisson random variable. The Geometric Random Variable. The Negative Binomial Random Variable. The Hypergeometric Random Variable
      5. Continuous Random Variables. Probability density function. The ccumulative distribution function. Expectation and variance of continuous random variables.
      6. The Uniform Random Variable. Normal Random Variables. Exponential Random Variables
      7. Jointly Distributed Random Variables
      8. Properties of Expectation. Conditional Expectation. Moment Generating Functions
      9. Covariance,Variance of Sums and Correlations
      10. Limit Theorems. Chebyshev’s Inequality and the Weak Law of Large Numbers.
      11. The Central Limit Theorem. The Strong Law of Large Numbers
      12. Sampling theory. Estimation theory
      13. Verification of statistical hypotheses
      14. Linear correlation analysis. Linear regression. Linear regression analysis
    • Computational Methods Applied in Economy - 6 ECTS
      1. General considerations: Key Concepts in Statistics, Applications of Statistics in Business
      2. Statistical series and graphics
      3. Computational models to analyze economic phenomena variation:The dimensional analysis, The two-dimensional analysis
      4. Computational models to analyze the causal links between phenomena: Correlation - concept, types, Basic Methods in correlation analysis, Analytical methods in correlation analysis
      5. Methods and techniques in computational analysis predicted: Time series. Definition and components, Methods and techniques of forecasting
      6. Methodology for calculating the key indicators of economic dynamics:Determination of aggregate macroeconomic indicators, Methodological aspects of assessing and measuring inflationary process, Aspects of measuring employment and unemployment, Methodology for calculating the balance of payments
      7. Determination of the dynamics of macroeconomic indicators: Evolution of the GDP, Dynamics inflationary process, The dynamics of employment and unemployment, Dynamic balance of payments
      8. Main interdependent macroeconomic and socio-economic implications: Correlation between inflation and GDP, The correlation between unemployment and GDP,The correlation between unemployment and inflation
    • Prognosis and Classification - 6 ECTS
      1. Introductory notions.
      2. Regressive models.
      3. Time series.
      4. Classification methods: Bayesian classification, Decision trees, k-nearest neighbour
      5. Clustering.
    • Sports - 1 ECTS
  •   2nd SEMESTER
    • Fundamental of Databases - 6 ECTS
      1. Concepts and database issues
      2. Designing logic databases
      3. Database with hierarchical and network structures
      4. Relational databases
      5. Data query languages for the relational model
      6. Restriction of integrity in databases: Functional dependencies,Multivalue dependencies
      7. Modelling relational databases: Normal forms in the database, Relations normalization techniques
      8. Physical structure of databases
      9. The integrity and security of databases
    • Network Security - 6 ECTS
      1. Information Security. Definition. Rate scheme. Presentation components. Principles of information security
      2. Security Policies: Privacy policy, integrity policy, hybrid policy, one study interference between security policies, Audit of information security
      3. Defining concepts and the links between: authentication, encryption, digital signature, CA, electronic voting. Coding versus encryption
      4. Symmetric key encryption:definition, cryptographic device
      5. Asymmetric key encryption. digital signature
      6. Encryption key management
      7. Protocols. Authentication protocols. AC. User authentication on the communication channel. Authentication scheme (Kerberos). Electronic money (digital cash). Blind transfer
      8. Viruses. Definition. Attack mode. Methods of protection: Trojan horse,worm,boot sector infectors,executable infectors,multi-viruses,encrypted viruses,macro viruses,macro viruses,abbits and bacteria,logic bombs
      9. Computer Network Security:firewalls,proxies,DMZ,mail server,WWW server,DNS server,log server,network flooding,intermediate hosts,CP state and memory allocations,anticipating attacks,Internet security protocols (PEM, S / MIME, PEM-MIME, MOSS, S-HTTP, SSL, PCT, S / WAN),
      10. Security database. OS security. Email security
      11. Ecommerce security
      12. Cryptanalysis
      13. Security Software. The development principles programs (secure design):Least privilege,fail-safe defaults,economy of mechanism,complete mediation,open design,separation of privilege,Least common mechanism,Psychological acceptability
      14. Steganography (hiding information in images). Hidden images signature
    • Operating Systems Development - 5 ECTS
      1. Concepts of operating systems. Structure of operating systems: monolithic systems, layered systems, virtual machines, client-server model. System calls: process management, signaling, file management, directory management, protection, time management. The internal structure of MINIX.
      2. Processes. Process management in MINIX. Interprocess communication in MINIX. Processes scheduling in MINIX. Implementations of processes in MINIX: common header files, data structures, system initialization, interrupt handling, hardware dependent support.
      3. Input/Output. Principles of I/O hardware. Principles of I/O software. Deadlocks. Overview of I/O in MINIX. Block devices in MINIX. Disks. Clocks. Terminals
      4. Memory management. Concepts. Overview of memory management in MINIX. Implementation of memory management in MINIX. Memory layout. Message handling. Memory manager data structures and algorithms. The FORK, EXIT, and WAIT system calls. The EXEC system call. Signal handling.
      5. File systems. Concepts. Protection mechanisms. Overview of the MINIX file system. Implementation of the MINIX file system: header files and global data structures, table management, operation on individual files, directories and paths, system calls.
    • Web Technologies - 5 ECTS
      1. Introduction to Internet:Web Characteristics,Web Clients and Servers,Formatting models for Web Documents
      2. Client/Server Model. HTTP Protocol
      3. Design web pages using X(HTML) language:(X)HTML Tags,Structure of (X)HTML documents,Using tables, frames and forms
      4. Formatting Web pages using Cascading Style Sheets (CSS)
      5. eXtended Markup Language (XML):XML Document Syntax: elements, attributes, comments,XML Document Validation, Document Object Model (DOM),Processing XML documents
      6. Formatting XML documents using XSLT
      7. WEB Applications Programming:Server-Side: CGI (Common Gateway Interface). PHP Language, Client-side: JavaScript, AJAX, Publishing databases on the Web
      8. Architecture, organization and maintenance of Web sites
      9. Web application security
      10. Search engine optimization (SEO)
      11. Web Services:Service-Oriented Architecture: SOA,Web Services Description Language: WSDL,SOAP Protocol
    • Dynamical Systems - 5 ECTS
      1. Definitions. ODE cases. Solutions - existence and uniqueness:Linear ODE;Qualitative aspects of the ODE;ODE systems (ODES) - linear ODES of the first order; ODE applications
      2. Numarical resolution of ODE. The Runge-Kutta method. The time step control. Numerical and graphical model study.
      3. Dynamical systems:Discret and continuous dynamical systems;Discrete dynamical systems;ontinuous dynamical systems
    • Applied Statistics - 5 ECTS
    • Practice - 3 ECTS
    • Sports - 1 ECTS

      3rd YEAR   

  •   1st SEMESTER
    • Methods of Algorithms Analysis - 6 ECTS
      1. Introduction to algorithms design. Methods for designing algorithms. The concept of algorithm. Features of algorithms
      2. Turing machine. Variants of the Turing machine model. Deterministic and non-deterministic Turing machine. Turing machines and formal languages
      3. Correctness of algorithms. Validate/verify algorithm correctness
      4. Analyze the algorithms efficiency. Complexity analysis of algorithms. Execution time. The order of growth
      5. Analysis of non-recursive algorithms. Estimate the time of execution (best-case, average-case, worst-case)
      6. Asymptotic analysis and asymptotic notations: o, O, W, w, Q. Properties of asymptotic notations. Complexity classes
      7. Analysis of recursive algorithms. Forward/backward substitution method. Iteration method. Master method. Trees recurrence method
      8. Analysis of algorithms. Case Study: Towers of Hanoi. Correctness and complexity analysis
      9. Inefficiency and incomputable algorithms
      10. NP-Completeness. Deterministic algorithms. P and NP classes. NP-complete problems
    • Computer Graphics - 5 ECTS
    • Databases - 5 ECTS
    • Knowledge Bases - 5 ECTS
      1. Aspects of knowledge representation: classes of knowledge, methods for knowledge representation and reasoning, knowledge bases, systems of knowledge representation and processing
      2. Structured knowledge representation and processing: knowledge bases with frames, queries
      3. Rewriting systems: Systems Lindenmayer, rewriting mechanisms: nodes rewriting and arcs rewriting, FASS curves
      4. Semantic Networks: labeled graphs, semantic schemas, WordNet semantic network
      5. Systems of knowledge representation and processing with production rules: Formalism. Inference Engine, Expert Systems
      6. Knowledge representation in natural language processing: Classes of grammars and languages, grammars in Prolog representation, dependency grammars, text analysis with syntactic constituents, meaning of words - automatic disambiguation
    • Genetic Algorithms - 5 ECTS
      1. Introduction to evolutionary computation : evolutionary computation specificity, fundamentals, areas of applicability
      2. Search space and fitness function: coding search space (data structures, encoding rules: binary, integer, real, specific), building fitness function.
      3. Selection methods: selection by rank, roulette method, proportionate fitness method, universal stochastic selection, truncation selection, local selection, tournament selection, comparison of selection methods
      4. Crossover operator: binary crossover, real crossover, adjacent crossover, path crossover, ordinal crossover, matrix crossover
      5. Mutation operator: binary mutation, real mutation, integer mutation, specific mutation
      6. Reinsertion: local reinsertion, global reinsertion
      7. Applications of genetic algorithms: optimization problems, NP-complete problems, applications in algebra
      8. Schemes and blocks: definitions, characteristics, scheme theorem, blocks
      9. Types of genetic algorithms: convergence problems, modified genetic algorithms, hillclimbing algorithm, simulated annealing algorithm, algorithms with variable size population, constrained algorithm, qualitative study
    • E-Commerce - 5 ECTS
      1. Internet business management.
      2. B2C E-commerce Model
      3. Introduction to Online Content Distribution
      4. Copyright Law
      5. Sharing the P2P files and Internet music
      6. XML and its relationship to B2B commerce
      7. C2C E-commerce model
      8. Internet Portals
      9. Web Searching and Googl
      10. The Open-source movement
      11. Identity Management: Enterprise, E-commerce and Government applications and their implications for privacy
      12. E-Mail Abuse: Spam and Viruses
    • Web Application Development - 5 ECTS
      1. Introduction in JSP and Tomcat
      2. Java Servlet Technology: Servlet Lifecycle; Information Sharing; Request Filtering; Sessions; Web Resources
      3. Java Server Pages Technology: Basics; Scripting in JSP; JSP Documents; JSTL and custom tags in JSP
      4. Java Server Faces Technology: Basic Components; Web Application Development with JSF Technology
      5. Java Persistence API
      6. Web Services: REST; SOAP
    • Bachelor Degree Paper (I) - 4 ECTS
  •   2nd SEMESTER
    • Parallel and Distributed Algorithms - 6 ECTS
      1. Parallel and distributed architectures: Examples of distributed architectures parallel, parallel computing systems and their classification, synchronization problems in parallel and distributed algorithms
      2. Synchronous algorithms:Algorithms for systems of linear equations and matrix inversion, iterative methods for nonlinear problems, unconstrained optimization - algorithms nonlinear, parallelization optimization problems, projection Algorithms
      3. Asynchronous algorithms: Asynchronous algorithms, partially asynchronous iterative methods, gradient type methods, organizing a network processor asynchronous distributed computing
    • Numerical Computation - 5 ECTS
      1. Equations and systems of linear equations: Newton's method, method of successive approximations, gradient method, Numerical methods for algebraic equations: Lobacevski method, Bairstow method, Bernoulli method, Steffenson method, calculating the limits and the number of real roots
      2. Matrix. Systems of linear equations: Matrix triangularization procedures, matrix factorization procedure - LR, QR, Direct methods for solving linear systems, iterative methods for solving linear systems, determinants and matrix inversion calculation
      3. Characteristic polynomial. Vectors and eigenvalues: Numerical methods for calculating the characteristic polynomial, jacobi and Givens methods for calculating and eigenvectors for symmetric matrices, method of power, LR and QR methods for calculating matrix and eigenvectors for some
      4. Interpolation and approximation: Procedures interpolation: Lagrange, Newton, interpolation by spline functions
      5. Finite differences. Numerical derivation: Finite differences - ascending, descending, central, numerical derivation methods
      6. Numerical integration: Newton methods for simple integrals, numerical methods for multiple integrals, gauss methods
    • Formal Languages, Automata and Compilers - 6 ECTS
      1. Grammars and languages: definitions, Chomsky classification, properties, recursion
      2. Finite automata: definition, representation, completely deterministic finite automata, finite automata minimization, finite automata and regular languages
      3. Context-free languages: properties, representation, simplification of the context free grammars
      4. Pushdown automata: definition, operation, accepted languages by pushdown automata, equivalence with context-free languages accepted by pushdown automata
      5. Special classes of context-free grammars: grammars in Chomsky normal form, non-recursive grammars, LL grammars, LR grammars, precedence grammars
      6. Translation of languages: structure of a compiler, syntax-directed translation, finite translators, pushdown translators
      7. Lexical analysis: overview, design of a lexical analyzer
      8. Syntactic analysis: general algorithms, LL analysis, LR analysis, precedence analysis
      9. Semantic analysis: semantic specification, semantic analysis model
      10. Intermediate code generation: syntax trees, intermediate code with three addresses
      11. Intermediate code optimization: simple optimization, global optimization, local optimization
      12. Object code: types of object code, code optimization
      13. Symbol table: types of tables, tree tables, hash tables
      14. Error handling: error sources, correction techniques
    • Non-Procedural Programming - 5 ECTS
      1. Introduction: Logic programming vs. functional programming; Examples
      2. Logic programming. Prolog language:Data structure in Prolog ;Built-in predicates;Unification and recursion;Lists in Prolog ; Compound terms in Prolog ; I/O in Prolog; Backtracking and cut in Prolog; Graphs and trees in Prolog; Characters and strings; Dynamic databases
      3. Functional programming. Lisp language:Numbers in Lisp; Lists; Arithmetic; Strings and characters; Symbols; Packages; Basic processing in Lisp; Function definition; Predicate functions; Conditional expressions; Recursion; Recursion
    • Programming Engineering - 5 ECTS
      1. The main phases of software development process:Software development life-cycle;Conceptualization;Analysis;Design;Evolution;Maintenance;
      2. The Unified Modelling Language (UML):Appearance and development;The use case diagram;The class diagram;The state diagram;The sequence diagram;The collaboration diagram;The activity diagram;Case Studies;
      3. Application:System requirements specification;Requirements Analysis;Analysis of field;Architectural Design;Detailed design;Design of the user interface;Implementation
      4. Coding standards
      5. Design Patterns:Structural Patterns: Decorator, Composite, Creational Patterns: Abstract Factory;Behavioural Patterns: Iterator;Fundamental design patterns: Interface
    • Visual Programming Environments - 4 ECTS
      1. Introductory Elements: History, Strategies in visual programming, visual programming languages classification
      2. Visual programming language theory. Formal specification of visual programming languages. Analysis of visual programming languages.
      3. Mechanisms of visual languages. Flow control, procedural abstraction, data abstraction.
      4. Analysis of visual programming environments. Chimera. Visual imperative programming by demonstration. Forms / 3. Visual programming based on spreadsheets.
      5. Analysis of visual programming environments. Prograph. Visual dataflow programming. Kids / Cocoa. Rule-based visual programming.
      6. Analysis of visual programming environments. Cube. 3D visual programming languages. Visual programming and abstraction.
      7. MIT visual programming environment (Google) App Inventor. Android system. Description, characteristics, capacities and limitations, types of applications.
      8. Apple Xcode visual programming environment. Description. Features, capabilities and limitations, types of applications.
      9. Visual programming environment Microsoft Visual C #. Description, characteristics, capacities and limitations, types of applications.
    • Designing Administration of Informatic Systems - 4 ECTS
    • Bachelor Degree Paper (II) - 4 ECTS

 

 

 

MSC Degree in Computer Science - Methods and Models in Artificial Intelligence

     1st YEAR

  •   1st SEMESTER
    • Advanced Data Bases - 8 ECTS
      1. Distributed databases
      2. Object-Oriented Databases
      3. Object-Oriented
      4. Deductive Databases
      5. Data warehousing
      6. Multidimensional databases
      7. OLAP (On-line Analytical Processing)
      8. Multimedia databases
      9. Temporal databases
      10. Spatial databases
      11. Decision support systems
    • Ordered Algebraic Structures - 8 ECTS
      1. Topisc in theory of categories
      2. The category of orderes sets
      3. The category of bounded distributive lattices
      4. The category of some ordered algebras of classical logic (Boole, Stone, Heyting, Hilbert, etc);
      5. The category of some ordered algebras of fuzzy logic (Residuated lattices, MV-algebras, BL-algebras);
    • Computer Vision - 8 ECTS
      1. OpenCV library. Instalation and generalities
      2. Loading, printing and saving images
      3. Creation of a GUI application using QT for image processing
      4. Accessing values for pixels from an image
      5. Image processing using classes
      6. The histogram of an image
      7. Defining regions of interest in images
      8. Image content detection using the histogram
      9. Transformation of images by morphological operations
      10. Image filtering
      11. Line, contour and component extraction
      12. Structural analysis
      13. Applications that use the web camera
    • Scientific Research Methodology in Computer Science (I) - 6 ECTS
      1. What is research?
      2. How to pick a research subject
      3. How to read scientific articles
      4. Problem formulation
      5. Evaluation and validation
      6. Conference and journal publishing
      7. Writing technical reports (incl. dissertation)
      8. Paper presentation
      9. Writing grant proposals
      10. Ethics
    • Exploratory Data Analysis - 8 ECTS
      1. Introduction: Data type. Descriptive statistics. Graphical representation of a Data set. Analysis of the distributions of variables. Statistical tests. (5 hours)
      2. Regressive models: Correlation matrix; scatter plot. Linear regression. Non linear regression (polynomial anf mixed exponential). Multilinear regression. Logistic regresion. Notions of survival analysis. Cox proportional hazard model. Additive models. (6 hours)
      3. Time series: Preliminary notions. Smoothing methods. Forecasting using the time series’ trend. Forecasting using time series’ trend and seasonal components. Dynamic models based on time series: adjustment models, autopredictive models, ARIMA model. (7 hours)
      4. Clustering: Similarity measures, k-means algorithm. Hierarchical clustering. (4 hours)
      5. Multivariate exploratory techniques: Factor analysis. Principal components analysis. Canonical analysis.Discriminant analysis. OLAP. Anomaly detections. (6 hours)
    • Human-Computer Interaction - 8 ECTS
      1. Human-Computer Interfaces
        • Interface and Interaction
        • Input/Output Devices
      2. Designing Human-Computer Interaction
        • Human Abilities
        • Interaction as Information Processing
        • Ergonomic Criteria
        • Interaction Styles
        • Definition of Usability. How to Improve Usability
      3. User Interfaces
        • Types of User Interfaces
        • Direct Manipulation Interfaces: Windows, Icons, Menus
        • Tools for Creating User Interfaces/li>
      4. Intelligent Dialogue Systems
        • The Understanding Computer
        • Intelligent Dialogue Systems
      5. Natural Language Interfaces
        • Computational Linguistics
        • Syntactic and Semantic Analysis
        • Machine Translation
      6. Voice-user Interfaces
        • Voice Synthesis Technologies: Speech Synthesis with Java FreeTTS, Natural Language Generation
        • Speech Recognition Technologies: Hidden Markov Models, Speech Understanding, Voice Recognition. Microsoft Speech API. Java Speech API
  •   2nd SEMESTER
    • Web Technologies and E-Learning - 8 ECTS
      1. Design process and specific programmes for e-learning (1.1. Computer assisted education. Brief history, definition,terminology, The introduction of technology in academia and e-learning, Examples of e-learning projects )
      2. Models and techniques for computer-assisted learning( Models of learning and delivery lessons with computer system, Tutorials, exercises, simulations, games, educational Web sites, Web technologies used for designing educational software, Open class and model of network learning, Video conferencing and on-line training)
      3. Delivery Technologies. Navigating documents in Internet(Educational programs, virtual universities, Distance learning projects, Web publications (car) instruction,Performance obtained with the help of online courses )
      4. Online education and distance education(Brief history on Internet, The concept of Hypertext,Finding information on the Web using search engines, Educational portals )
      5. ISDN Networks: virtual network Environments and e-learning(Designing Web pages, Educational Web pages. Principles of organization and design, Performing applications to create Web)
      6. Publishing online a website (From offline to online: final preparation of a website, Promoting a Web site, Updating HTML documents, Identity management components, Feed-back Web site )
    • Data Mining - 8 ECTS
    • Scientific Research Methodology in Computer Science (II) - 6 ECTS
      1. Steps in scientific research
      2. Study manner in Computer Science research domain
      3. Components of a scientific paper
      4. Consistency in scientific research
      5. Team work in a scientific research project
      6. Scientific research team management
      7. The development of research projects for national and international competitions
      8. Scientific papers editing
      9. Scientific presentations preparation
    • Deductive Systems in Artificial Intelligence - 8 ECTS
      1. Deductive systems in classical logic
      2. Deductive systems in some fuzzy logic
      3. Deductive systems in some algebras of classical logic (Hilbert, Heyting, Boole, etc);
      4. Deductive systems in some algebras of fuzzy logic (Residuated lattices, Wajsberg algebras, MV-algebras, BL-algebras, pseudo MV – algebras, pseudo BL –algebras, etc).

 

      2nd YEAR   

  •   1st SEMESTER
    • Neuronal and evolutionary Computing - 8 ECTS
      1. Introduction to neural computation: characteristics of neural networks, history of neural computation, biological neuron, architecture and usage of a neural netw
      2. Networks with a single layer: simple perceptron, variants of simple perceptron, multiple perceptron;
      3. Unidirectional multi-layer networks: back propagation with one hidden layer, back propagation with multiple hidden layers, variants of back propagation
      4. Associative memories : linear association networks type, bidirectional associative memories (MAB), types of MAB, Hopfield networks with discrete time, auto-associative recurrent memory
      5. Networks with radial activation functions: network structure, network training
      6. Set of cooperating neural networks: operating principle , architecture, training
      7. Introduction to evolutionary computation: generalities, basic notions, encoding methods, selection methods
      8. Evolution operators: selection, crossover, mutation, reintegration
      9. Special classes of genetic algorithms: contraction algorithm, algorithms with varying population size, constraints algorithms, messy genetic algorithms, virus – evolutionary genetic algorithms
      10. Evolutionary strategies : generalities, evolutionary operators, types of evolutionary strategies, convergence study
      11. Evolutionary programming : generalities, representation of individuals, applications in optimization problems
      12. Genetic programming: representation of individuals , initial population, evolution operators, running programs
    • Web Data Management - 8 ECTS
    • Bioinformatics - 8 ECTS
      1. Foundations of computational biology.
      2. Comparing genes: a. Common maximal subsequence problem. Graphical methods comparison of genes b. Gene alignment by dynamic programming
      3. Genetic classification. Clustering methods
      4. Gene evolution. Trees of phylogeny.
      5. Molecular Computing. Autonomous and non-autonomous DNA computing models
    • Multiagent Systems - 8 ECTS
      1. Getting started with multi-agent systems. Descriptions, Applicability.
      2. Agents Architectures. Classifications, BDI architecture, mobile agents, intelligent agents.
      3. Modeling agents reasoning. Game theory, motion charts.
      4. Modeling agents reasoning. Nash equilibrium games in cooperating with agents, sequential games.
      5. Communication in multi-agent systems. Types of communication in multi-agent systems. Communication languages for agents. Ontologies.
      6. Negotiating in multi-agent systems. Problems negotiation. Approaches based on game theory.
      7. Negotiating in multi-agent systems. Approaches based on heuristics. Argument based approaches.
      8. Multi-agent systems for knowledge processing. MAKPS system structure, description of system components, system communication, system functionality.
      9. Multi-agent systems development platforms. JADE, Jason.
    • Disertation Paper Drafting (I) - 6 ECTS
  •   2nd SEMESTER
    • Intelligent Systems for Control and Classification - 8 ECTS
      1. Fuzzy sets: basic notions, operations (fundamental, based on t-operators)
      2. Fuzzy numbers, fuzzy relations
      3. Uncertainty: possibility and necessity measures, belief and plausability functions, Dempster’s rule
      4. Knowledge representation: linguistic variables, fuzzy implications, rules representation
      5. Reasoning methods: generalized modus ponens, uncertain and imprecise reasoning methods
      6. Fuzzy logic control systems: structure, types of fuzzy control systems (Mamdani, Tsukamoto, Sugeno, Larsen), extended systems
      7. Adaptive Neuro Fuzzy Inference System (ANFIS)
      8. Evolutionary neural networks: parameters evolution, architecture evolution, evolutionary learning
      9. Neuro - evolutionary classification methods: neural network structure, evolutionary determination of classification rules, refining of extracted rules
    • Mathematical Optimizations in Artificial Intelligence - 8 ECTS
    • Machine Learning - 8 ECTS
      1. Introduction
      2. R - the language and software
      3. Supervised learning - an overview
      4. The design of learning machines
      5. Linear methods for classification and regression
      6. Support vector machines
      7. Neural networks
      8. Decision trees
      9. Ensemble methods
      10. Clustering
      11. Evaluation and selection of models
      12. Missing data
      13. Variable selection and reduction
    • Advanced Methods of Chryptographic Analysis - 8 ECTS
      1. The notion of security systems analysis. Definition. Classical models. Current models
      2. Security level audit
      3. Security policies in distributed systems
      4. Cryptographic Analysis
      5. Symmetric encryption systems
      6. Multidimensional
      7. Asymmetric encryption systems)
      8. Pseudorandom generator systems
      9. Digital signatures
      10. Steganography
      11. Security systems in computer networks
      12. Banking Security Systems (Online Payments, Cards
      13. Standards and methodologies of defense against security attacks information systems Security models used in environments maximum protection
      14. User level security
      15. The study of individual computer systems security
      16. Models used cryptographic security network structures
      17. Security in computer networks. WAN
      18. Security of parallel computing
      19. Implementation of classical models in distributed systems
      20. Methods of ensuring information security in order to protect against Viruses, Worms and Trojan Horses
      21. Detection of network systems penetration
      22. Application layer security
      23. Systems to ensure security at the hardware level
    • Disertation Paper Drafting (II) - 6 ECTS

 

 

MSC Degree in Computer Science - Advanced Techniques for Information Processing

     1st YEAR

 

      2nd YEAR   

  •   1st SEMESTER
    • Machine Learning - 7 ECTS
      1. Introduction
      2. R - the language and software
      3. Supervised learning - an overview
      4. The design of learning machines
      5. Linear methods for classification and regression
      6. Support vector machines
      7. Neural networks
      8. Decision trees
      9. Ensemble methods
      10. Clustering
      11. Evaluation and selection of models
      12. Missing data
      13. Variable selection and reduction
    • Software Engineering - 8 ECTS
    • Optimization Techniques - 7 ECTS
      1. One-dimensional line search descent methods: Golden section, Powell’s quadratic interpolation, bisection method, Newton’s method
      2. First order multidimensional line search descent methods: steepest descent method, Fletcher-Reeves conjugate gradient
      3. Second order multidimensional line search descent methods: modified Newton's method, quasi-Newton methods
      4. Constrained non-linear optimization: Kuhn-Tucker conditions, penalty methods, admissible steering method
      5. Introduction to evolutionary computation: basic notions, coding methods, evolution operators (selection, crossover, mutation, reinsertion)
      6. Special classes of genetic algorithms: contraction algorithms, variable population algorithms, constrained algorithms, messy genetic algorithms, virus evolutionary genetic algorithm
      7. Evolutionary strategies: generalities, evolutionary operators, evolutionary strategies types, convergence study
      8. Evolutionary programming: general aspects, population representation, optimization applications
    • Advanced Topics in Computer Networks - 7 ECTS
    • Geographic Information Systems - 5 ECTS
    • Distributed Architectures Web Services Oriented - 5 ECTS
    • Dissertation Preparation - I - 3 ECTS
  •   2nd SEMESTER