Courses

PPCIC students must perform a total of at least 24 (twenty-four) credits, distributed as follows:

  1. 9 (nine) credits in classes from the basic group, chosen in agreement with the student tutor;
  2. 15 (fifteen) credits classes from the specific group, chosen in agreement with the student tutor.

Table 1 presents the main classes offered by PPCIC professors. The course syllabus is presented after the table. It is worth noticing that each course presents basic references that are complemented by scientific papers, which are more updated than the text books.

Table 1 – PPCIC Classes

Course Group Credits
Analysis and Design of Algorithms Basic 3
Computational Linear Algebra Specific 3
Computer Architecture Basic 3
Data Mining Specific 3
Database Systems Basic 3
Foundations on Multimedia Systems Specific 3
Graph Algorithms Specific 3
Large-Scale Data Management Specific 3
Linear Algebra and Graphs Specific 3
Machine Learning Specific 3
Metaheuristic Optimization Specific 3
Operational Research Specific 3
Parallel and Distributed Computing Basic 3
Process Mining Specific 3
Robotics Applications Specific 3
Scientific Methodology in Computer Science Basic 3
Statistical Methods Basic 3
Text Mining Specific 3
Topics in Algorithms Specific 3
Topics in Computational Intelligence Specific 3
Topics in Data Management Specific 3
Topics in Modeling Specific 3
Topics in Multimedia Specific 3
Topics in Optimization Specific 3
Masters Dissertation Research 0
Masters Dissertation Seminar 0

 

Analysis and Design of Algorithms

Data structures, specification of algorithms and analysis of computational complexity. The general methods of data organization: hashing, trees, queues, lists, priority queues and their applications in problems of graph searches, optimization and large-scale scientific computation are presented.

  1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.
  2. Jon Kleinberg and Eva Tardos. Algorithm Design. Pearson, Boston, 1 edition, March 2005.
  3. Robert Sedgewick and Kevin Wayne. Algorithms. Addison-Wesley Professional, Upper Saddle River, NJ, 4th edition, March 2011.
  4. Donald E. Knuth. The Art of Computer Programming. Addison-Wesley Professional, Amsterdam, March 2011.

 

Computational Linear Algebra

Basic Aspects (Vector Spaces, Matrix Algebra, Norms, and complexity). Error analysis and matrix condition number. Gauss elimination and Decomposition techniques. Jacobi and Gauss-Seidel Methods. Krylov Methods. Eigenvalues and Eigenvector problems. Multigrid Methods. Preconditioning techniques. Introduction to parallelism in Linear Algebra.

  1. G. Golub & C. vanLoan, Matrix Computations; Johns Hopkins University Press;
  2. P.G. Ciarlet, Introduction a l’Analyse Numerique Matricielle et a l’Optimisation; Ed.Masson
  3. D.S. Watkins. Fundamentals of Matrix Computation. Wiley-Interscience
  4. A Multigrid Tutorial – William L. Briggs, Van Emden Henson, Steve F. McCormick. SIAM. 2nd Edition
  5. Graph Algorithms in the Language of Linear Algebra – Jeremy Kepner & John Gilbert. SIAM. 1st Edition.

 

Computer Architecture

Introduction to computer organization. Numbering systems. Memory hierarchies. Main, cache and read-only memory. Central Processing Unit: components, instruction cycle. Input and output methods and devices.

  1. Andrew S. Tanenbaum and Todd Austin. Structured Computer Organization. Pearson, Boston, 6 edition, August 2012.
  2. William Stallings. Computer Organization and Architecture: International Edition. Pearson Education, edic~ao: 9 edition, March 2013.
  3. John L. Hennessy and David A. Patterson. Computer Architecture: A Quantitative Approach. Morgan Kaufmann Publishers, Waltham, MA, September 2011.

 

Data Mining

Mining is the process of extracting knowledge from data. The main topics covered in this course include pre-processing, sorting, grouping, membership rules, anomaly, and the data mining process itself. The discipline aims to provide students with the fundamental skills needed to conduct their own research in data mining.

  1. Mohammed J. Zaki and Wagner Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, May 2014.
  2. Ian H. Witten, Eibe Frank, and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA, 3 edition, January 2011.
  3. Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, Waltham, Mass., 3 edition, July 2011.
  4. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer, 1st edition, August 2013.
  5. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition, April 2011.
  6. Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, softcover reprint of hardcover 2nd ed. 2011 edition, August 2013.

 

Database Systems

Basic concepts of DB: Introduction to basic concepts of database management. The architecture of a DBMS. Evolution of the data models. The relational model: relationship algebra and query optimization. Transactions and ACID properties. Concurrency control. Fault recovery. Distributed DBs: concepts, data distribution design, distributed query processing. NoSQL: CAP theorem, ACID vs BASE, key-value data models, columnar, documents and graphs. DBMS approaches In-Memory DB, Space-Time BDs, MOD (Moving Objects Databases).

  1. Ramez Elmasri and Shamkant B. Navathe. Fundamentals of Database Systems. Pearson / Addison Wesley, Boston, 5th edition, March 2006.
  2. Abraham Silberschatz, Henry Korth, and S. Sudarshan. Database System Concepts. McGraw-Hill Science/Engineering/Math, New York, 6 edition, January 2010.
  3. C. J. Date. An Introduction to Database Systems. Pearson, Boston, Mass., 8 edition, August 2003.
  4. Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. McGraw-Hill, Boston, Mass., 3rd edition, August 2002.

 

Foundations on Multimedia Systems

Introduction to multimedia systems. Presentation of the concept of a media item, together with its representation, storage, and exhibition. Discussion about the different components of a multimedia system. Multimedia authoring models and languages. Multimedia in the Web, Digital TV, and IPTV. Synchronism of things.

  1. Multimedia Communications: Applications, Networks, Protocols, and Standards. F. Halsall, Addison-Wesley Publishing, 2000.
  2. Programming in NCL 3.0, Soares, L.F.G.S.; Barbosa, S.D.J. Editora Campus-Elsevier, 2009.
  3. Foundations on Multimedia Systems. Soares, L.F.G.; Tucherman, L.; Casanova, M.A.; Nunes, A. VIII Escola de Computação, julho 1992.

 

Graph Algorithms

Analysis of algorithms. Representation Schemes for Graphs. Paths in Graphs. Applications of Paths in Graphs. Topological Ordering. Greedy Algorithms. Dynamic Programming. Minimum Generating Tree. Minimal Paths. Maximum Flow and Maximum Pairing.

  1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.
  2. Robert Sedgewick and Kevin Wayne. Algorithms. Addison-Wesley Professional, Upper Saddle River, NJ, 4th edition, March 2011.
  3. Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani. Algorithms. McGraw-Hill Education, Boston, 1 edition, September 2006.

 

Large-Scale Data Management

The introduction of fundamental concepts, technologies and innovative applications made to the processing and analysis of large data volumes (BigData). It explores the latest technology solutions, including different forms of data organization, including Distributed Storage Systems (HDFS) approaches, object-relational databases, NoSQL and new SQL, and their connections as a parallelism technique based on data partitioning.

  1. M. Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database Systems. Springer, New York, 3rd ed. 2011 edition, March 2011.
  2. Peter Lake and Robert Drake. Information Systems Management in the Big Data Era. Springer, New York, NY, 2014 edition, January 2015.
  3. Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives. Pearson FT Press, Upper Saddle River, 1 edition, May 2014.
  4. Hrushikesha Mohanty, Prachet Bhuyan, and Deepak Chenthati, editors. Big Data: A Primer. Springer, New York, NY, 2015 edition, July 2015.
  5. Aboul-Ella Hassanien, Ahmad Taher Azar, Vaclav Snasel, Janusz Kacprzyk, and Jemal H. Abawajy, editors. Big Data in Complex Systems: Challenges and Opportunities. Springer, New York, 2015 edition, January 2015.
  6. Christine L. Borgman. Big Data, Little Data, No Data: Scholarship in the Networked World. The MIT Press, Cambridge, ScienticMassachusetts, January 2015.
  7. Sandya Mannarswamy. Data Science: Learn the What, Where, and How of Data Science. Apress, 2015 edition, June 2015.
  8. Tony Hey, Stewart Tansley, and Kristin Tolle, editors. The Fourth Paradigm: Data-InScientific Discovery. Microsoft Research, Redmond, Washington, 1 edition, October 2009.

 

Linear Algebra and Graphs

The objective of the discipline is to study the main results of the literature that involve eigenvalues and eigenvectors of Hermitian matrices. Several of these results are of great theoretical relevance and have great application in problems that arise in Spectral Theory of Graphs. The topics covered in the course are described below:

  • Revision of basic concepts in Linear Algebra (1.1 Vector Spaces, 1.2 Linear Transformation, 1.3 Determinant, 1.4 Special Arrays)
  • Eigenvalues and eigenvectors (2.1 Eigenvalue-eigenvector equation, 2.2 Characteristic polynomial, 2.3 Eigenvectors)
    Hermitian and symmetric matrices (3.1 Definitions, properties and characterization 3.2 Hermitian matrix eigenvalues 3.3 Applications)
  • Theorems in eigenvalues of Matrices (4.1 Min-Max Principle of Courant-Fischer-Weyl; 4.2 Weyl Inequalities; 4.3 Cauchy Interlace Theorem; 4.4 Perron-Frobenius Theorem)
  • Symmetric matrices associated with graph (5.1 Adjacency and its properties 5.2 Laplacian and its properties 5.3 Laplacian without sign and its properties)
  1. Matrix Analysis, Roger A. Horn e Charles R. Johnson, Cambridge University Press, 1999.
  2. Matrix Analysis, Rajendra Bhatia, Springer, 1996. [1] Algebraic Graph Theory, Chris Godsil e Gordon Royle, Springer, 2004.
  3. Eigenspaces of graphs, Dragos Cvetkovic, Peter Rowlinson, Slobodan Simic, Cambridge University Press, 1997.
  4. Algebraic Graph Theory, Chris Godsil e Gordon Royle, Springer, 2004.
  5. Algebraic Graph Theory, Norman Biggs, Second Edition, Cambridge University Press, 1994.

 

Machine Learning

Machine learning is a fast-growing field on the frontier between computer science and statistics whose goal is to find patterns from data. In this discipline, we study a range of methods: connectivist, probabilistic, proximity-based, decision trees that can be used in different stages of the data-based experimentation process.

  1. Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition, August 2012.
  2. Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge ; New York, 1 edition, November 2012.
  3. Christopher Bishop. Pattern Recognition and Machine Learning. Springer, New York, October 2007.
  4. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer, 1st ed. 2013. corr. 4th printing 2014 edition, August 2013.
  5. Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning From Data. AMLBook, S.l., March 2012.
  6. Brett Lantz. Machine Learning with R. Packt Publishing, Birmingham, October 2013.
  7. Simon O. Haykin. Neural Networks and Learning Machines. Prentice Hall, New York, 3 edition, November 2008.

 

Metaheuristic Optimization

Several computational problems lie in exploring solutions in a non-polynomial search space. In these scenarios, heuristics to find approximate solutions are commonly employed. The course includes: (i) Introduction to algorithm analysis and complexity theory; (Ii) constructive heuristics and greedy algorithms; (Iii) Local search methods; (Iv) Metaheuristics: fundamentals; (V) simulated annealing algorithm; (Vi) Tabu search; (Vii) Greedy randomized adaptive search procedures (GRASP); (Viii) Genetic algorithms.

  1. Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, New York u.a, 1st edition, January 1979.
  2. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.
  3. Nils J. Nilsson. Principles of Artificial Intelligence. Springer, Berlin, softcover reprint of the original 1st ed. 1982 edition, April 2013.
  4. Ibrahim H. Osman and James P. Kelly, editors. Meta-Heuristics: Theory and Applications. Springer, Boston, 1996 edition, March 1996.

 

Operacional Research

The discipline of operational research can be understood as applied mathematics, where mathematical models, statistics, and algorithms are used to aid in decision making. It has a strong association with the problems of Computer Science, where it aims to improve or optimize a particular model. The course includes (i) Introduction to Linear Programming (modeling, graphical solution, simplex and its variants); (Ii) Introduction to Integer Programming (Modeling, Resolution Methods); (Iii) Introduction to Game Theory and prediction models; (Iv) Introduction to Markov chain and queue theory.

  1. Wayne L. Winston. Operations Research: Applications and Algorithms. Duxbury Press, Belmont, CA, 4 edition, July 2003.
  2. Mokhtar S. Bazaraa, John J. Jarvis, and Hanif D. Sherali. Linear Programming and Network Flows. Wiley, Hoboken, N.J, 4 edition, December 2009.
  3. Laurence A. Wolsey. Integer Programming. Wiley-Interscience, New York, 1 edition, September 1998.

 

Parallel and Distributed Computing

Systems, architectures, algorithms, programming models, languages and software tools. Topics covered include parallelization and distribution models (MPI, Map-Reduce, etc.); Parallel architectures; Cluster and parallel and distributed computing systems, distributed and parallel algorithms, data structures and programming methodologies; applications; And performance analysis.

  1. Georg Hager and Gerhard Wellein. Introduction to High-Performance Computing for Scientists and Engineers. CRC Press, Boca Raton, FL, 1 edition, July 2010.
  2. Victor Eijkhout. Introduction to High-Performance Scientific Computing. lulu.com, Raleigh, N.C., January 2015.
  3. K. G. Srinivasa and Anil Kumar Muppalla. Guide to High-Performance Distributed Computing: Case Studies with Hadoop, Scalding, and Spark. Springer, New York, NY, 2015 edition, February 2015.
  4. Mahmoud Parsian. Data Algorithms: Recipes for Scaling Up with Hadoop and Spark. O’Reilly Media, Sebastopol, 1 edition, July 2015.
  5. Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills. Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O’Reilly Media, Beijing, 1 edition, April 2015.
  6. Pethuru Raj, Anupama Raman, Dhivya Nagaraj, and Siddhartha Duggirala. High-Performance Big-Data Analytics: Computing Systems and Approaches. Springer, S.l., 2015 edition, August 2015.
  7. Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives. Pearson FT Press, Upper Saddle River, 1 edition, May 2014.

 

Process Mining

Concepts of business process modeling (BPM). Process models and business process discovery. Different types of process models. Process discovery techniques and compliance analysis. Enrichment of process models. Operational support.

  1. VAN DER AALST, Wil. Process Mining: Data Science in Action. 2nd Springer-Verlag, 2016.
  2. MANS, Ronny S., VAN DER AALST, Wil, VANWERSCH, Rob, J. B. Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes. Springer Cham Heidelberg, 2015.
  3. Beheshti, Seyed-Mehdi-Reza, Benatallah, Boualem, Sakr, Sherif, Grigori, Daniela, Motahari-Nezhad, Hamid Reza, Barukh, Moshe, Chai, Gater, Ahmed, Ryu, Seung Hwan. Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data. Springer International Publishing, 2016.
  4. Burattin, Andrea. Process Mining Techniques in Business Environments: Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining (Lecture Notes in Business Information Processing). Series: Lecture Notes in Business Information Processing (Book 207). Springer; 2015.
  5. Provost, Foster, Fawcett, Tom. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, 2013.
  6. Han, Jiawei, Kamber, Micheline, Pei, Jian. Data Mining: Concepts and Techniques. 3rd edition. The Morgan Kaufmann Series in Data Management Systems, 2011.

 

Robotics Applications

Fundamentals and General Characteristics of Robotics; Industrial and Mobile Robots; Sensors, Actuators and Manipulators; Concepts of Microcontrollers: Types, Characteristics, Internal Organization, Programming Languages. Robot Modeling Furniture (kinematic and dynamic). Programming strategy of microcontrollers for mobile robots. Robotics Applications: Mobile Robots for Education and IoT (Internet of Things).

  1. NIKU, Saeed B.. Introduction to robotics analysis, systems, applications. c2001. 349 p. ISBN 0-13-061309-6. Upper Saddle River, NJ.: Prentice-Hall.
  2. MARTINS, N. A.. Sistemas Microcontrolados. 1a ed., Novatec, 2005.
  3. CRISP, J.. Introduction to Microprocessors and Microcontrollers. 2a ed., Newnes, 2004
  4. MACKENZIE, I. S.; PHAN, R. C. W.. The 8051 Microcontroller. Prentice-Hall, 2006.
  5. GILLILAND, M.. The Microcontroller Application Cookbook. Woodglen Press, 2000.
  6. WILMSHURST, T.. Designing Embedded Systems with PIC microcontrollers: principles and applications. Newnes, 2006.

 

Scientific Methodology in Computer Science

The course aims to develop skills for the elaboration of articles and scientific projects in Computer Science. Thus, it is important that the student is aware of the importance of the main elements related to the research, since the choice of subject, problem definition, literature review, research execution to the writing process. Summary: (i) research preparation; (ii) literature review; (iii) citations; (iv) scientific writing; (v) graphs, figures and tables; (vi) presentations; (vii) mathematics formalization and algorithms; (viii) experimental evaluation planning and hypothesis formulation; (ix) research execution; (x) plagiarism, (xi) written in English.

  1. Justin Zobel. Writing for Computer Science. Springer, New York, NY, 3rd ed. 2014 edition, February 2015.
  2. Raul Wazlawick. Metodologia de Pesquisa para Ciência da Computação. Elsevier, edic~ao: 2 edition, September 2014.
  3. Hilary Glasman-Deal. Science Research Writing for Non-Native Speakers of English. Imperial College Press, London ; Hackensack, NJ, 1 edition, December 2009.

 

Statistical Methods

Introduction to uni-varied data analysis methods. Descriptive statistics and exploratory data analysis methods. Overview of sampling techniques for data collection and introduction to statistical inference methods for decision making, including simple linear regression, estimation procedures using confidence intervals, and hypothesis testing.

  1. Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2nd edition, August 2008.
  2. Richard J. Larsen and Morris L. Marx. An Introduction to Mathematical Statistics and Its Applications. Prentice Hall, Upper Saddle River, N.J, 4 edition, December 2005.
  3. Ronald E Walpole. Probability & statistics for engineers & scientists. Prentice Hall, Boston, 2012.
  4. Jay L. Devore and Kenneth N. Berk. Modern Mathematical Statistics with Applications. Springer, New York ; London, 2nd ed. 2012 edition, December 2011.

 

Text Mining

Overview of text mining and applications. Natural language processing and document representation. Knowledge Discovery Process in Text (KDT). Exploratory Text Analysis. Pre-processing of Text: Stopwords; Stemming; Dictionary or Thesaurus. Grouping and classification of texts. Sentiment analysis and Opinion Mining. Evaluation Metrics.

  1. Ronen Feldman and James Sanger. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 1 edition, December 2006.
  2. Matthew L. Jockers. Text Analysis with R for Students of Literature. Springer, New York, July 2014.
  3. Anne Kao and Steve R. Poteet. Natural Language Processing and Text Mining. Springer London, 1 edition, March 2007.
  4. Christopher Manning and Hinrich Schuetze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Mass, 1 edition, June 1999.
  5. Charu Aggarwal and ChengXiang Zhai, editors. Mining Text Data. Springer, edição: 2012 edition, February 2012.
  6. Gary Miner, John Elder, IV, Andrew Fast, Thomas Hill, Robert Nisbet, and Dursun Delen. Practical Text Mining and Statistical Analysis for Nonstructured Text Data Applications. Academic Press, edição: 1 edition, January 2012

 

Topics in Algorithms

Development of algorithms and complexity analysis; mathematical induction methods and induction algorithm designs; projects of efficient algorithms in computational problems; algorithms in graphs; approach to elementary and advanced data structures; design and analysis of adaptive algorithms.

  1. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Algoritmos: teoria e prática. Tradução da 3ª Edição Americana, Elsevier Editora LTDA, 2012.

 

Topics in Computational Intelligence

Development of prototypes, algorithms or computational artifacts involving features of computational intelligence applications (such as data mining, text mining, process mining, machine learning and statistical learning) associated with data models present in Data Science (such as big data, time series, space-time series, streaming, images, texts) in areas such as health, education, economics, transport, robotics, social networks, cognition and feelings. These prototypes will make use of one or more specialized computational intelligence methods in some clipping of these models/domains.

  1. Han, Jiawei, Kamber, Micheline, Pei, Jian. Data Mining: Concepts and Techniques. 3rd edition. The Morgan Kaufmann Series in Data Management Systems, 2011.
  2. Rutkowski, Leszek (2008). Computational Intelligence: Methods and Techniques. Springer. ISBN 978-3-540-76288-1.
  3. VAN DER AALST, Wil. Process Mining: Data Science in Action. 2nd edition. Springer-Verlag, 2016
  4. KAO, Anne; POTEET, Stephen; Natural language processing and text mining. London: Springer 2007. ISBN 184628175.

 

Topics in Data Management

Development of prototypes, algorithms or computational artifacts involving data management of different models and at different scales (including Big Data) in different Data Science and architecture contexts (centralized, parallel and distributed). These prototypes will make use of one or more specialized methods of data management in some data modeling and application domains.

  1. M. Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database Systems. Springer, New York, 3rd Ed. 2011 Edition, March 2011.
  2. Peter Lake and Robert Drake. Information Systems Management in The Big Data Era. Springer, New York, Ny, 2014 Edition, January 2015.
  3. Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time Applications With Storm, Spark, And More Hadoop Alternatives. Pearson Ft Press, Upper Saddle River, 1 Edition, May 2014.
  4. Aboul-Ella Hassanien, Ahmad Taher Azar, Vaclav Snasel, Janusz Kacprzyk, And Jemal H. Abawajy, Editors. Big Data in Complex Systems: Challenges And Opportunities. Springer, New York, 2015 Edition, January 2015.
  5. Tony Hey, Stewart Tansley, And Kristin Tolle, Editors. The Fourth Paradigm: Data-Intensive Scientic Discovery. Microsoft Research, Redmond, Washington, 1 Edition, October 2009.

 

Topics in Modeling

Modeling Techniques; Computational Simulation Techniques; Complexity Analysis; Applications in Engineering and Science Problems.

  1. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Algoritmos: teoria e prática. Tradução da 3ª Edição Americana, Elsevier Editora LTDA, 2012.
  2. Shiflet, A.B., Shiflet, G.W. Introduction to Computational Science: Modeling and Simulation for the Sciences. Second Edition, Princeton University Press, 2014.

 

Topics in Multimedia

This course covers the most relevant topics in the moment in the multimedia area. It discusses concepts, characteristics, standards and requirements for modeling multimedia applications in different contexts. Discussions encompasses, but are not limited to: Internet of Things, Sensory Effects and related areas.

  1. YOON, Kyoungro et al. “MPEG-V: Bridging the Virtual and Real World”. Academic Press, 2015.
  2. FURHT, Borko (Ed.). “Multimedia Systems and Techniques”. Springer Science & Business Media, 2012.
  3. WALTL, Markus. “Enriching multimedia with sensory effects: annotation and simula- tion tools for the representation of sensory effects”. VDM Verlag, 2010.
  4. HALSALL, Fred. “Multimedia communications: Applications, networks, protocols and standards”. Pearson education, 2001.

 

Topics in Optimization

Approach of exact methods for solving linear and non-linear programming problems; implementation of heuristics and metaheuristics for the resolution of problems of the nature of Computer Science;

  1. Glover, F., Kochenberger, G.A., Handbook of Metaheristics, Kluwer Academic Publishers, 2002.

 

Masters Dissertation Research

The student, after approved in the Masters Dissertation Seminar, must register in this class in order to continue the research to be presented in his(her) dissertation. This class does not count credits for the student.

 

Masters Dissertation Seminar

The student must be registered at the Masters Dissertation Seminar class during the elaboration and presentation of his(her) dissertation proposal. The student must elaborate and present a Masters Dissertation Proposal during the course of this class. This class does not count credits for the student.