Curricular Unit: Code: Algorithms and Data Structures II 1093AED2 Year: Level: Course: Credits: 2 Undergraduate Computer Systems Engineering 6 ects Learning Period: Language of Instruction: Total Hours: Spring Semester Portuguese/English 78 Learning Outcomes of the Curricular Unit: Upon successful completion of this course unit students should be able to (learning outcomes - LO):LO1-Understand the notion of symbol tables and their applicabilityLO2-master the notion of binary search trees and articulate associated algorithmsLO3-Master the notion of tree balancing using red-black treesLO4-distinguish the use of hash tables, hash functions and their propertiesLO5-distinguish and apply the notion of graph in algorithmic modelingLO6-master and use directed graphs in various algorithmic problemsLO7-apply minimum spanning tree algorithms and their propertiesLO8-explain and apply shortest path calculation algorithms in graphs Syllabus: 1. Introduction to non-linear data structures1.1. Introduction to trees and graphs1.2. Notation and Terminology1.3. Representation1.4. Applications of non-linear data structures2. Search and tree data structures2.1. Symbol tables2.2. Introduction to Tree Data Structures2.3. Binary search trees (BST - binary search trees)2.4. Balanced trees2.5. Hash tables3. Graphs3.1. Introduction to the graph data structure and its representation3.2. Graph traversal algorithm3.3. Undirected graphs3.4. Directed graphs3.5. Weighted graphs3.6. Weighted and directed graphs Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives: The syllabus presented are consistent with the learning objectives of the curricular unit since there is a large convergence between the table of contents and the knowledge that the student is supposed to acquire in each of the program topics.The fundamental concepts of algorithmic analysis and design and nonlinear data structures are presented in the introductory chapter, in the following chapters are presented various algorithms and data structures for searching and also graph algorithms.The learning objectives are achieved by supplementing the theoretical concepts with concrete examples run in lab environment using appropriate software Teaching Methodologies (Including Evaluation): This Course Unit (UC) is classified as a Project. Includes core competences that are not subject to examination. There are elements of continuous assessment whose weighted average is required to be positive, the Practical Score of Continuous Assessment (NPAC)Possible assessment results:a) Student achieves minimum goals (NPAC >= 9.5 values) and a positive Final Grade (NF1 >=9.5 values) in continuous assessment. Approves the UC with NF1b) Student achieves minimum goals (NPAC >= 9.5 values) and (NF1 < 9.5 values). Can be assessed on examination. Exam assessment is independent of continuous assessment. UC Final Grade is NF2c) Student does not achieve minimum goals (NPAC < 9.5 values). Does not pass the UC and will not be able to access the examExpected assessment elements:A1. Test 1A2. Test 2A3. Practical projectA4. ExamContinuous Assessment Model:NPAC = A3NF1 = (A1 + A2 + NPAC)/3, NPAC >= 9.5Exam Assessment Model (NPAC >= 9.5):NF2 = A4 Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes: The teaching/learning methodology applied in this curricular unit as well as its evaluation system is perfectly aligned with the objectives to be attained by the students at the end of the term. The theoretical concepts are presented, discussed, applied and evaluated in the context of lectures, which guarantees students a solid foundation to understand the challenges facing this area of knowledge. On the other hand, so that the study is not restricted to conceptual models, in the practical lessons are presented concrete case studies and implemented solutions for real problems using appropriate software. This combination guarantees training for students that allows them to meet the scientific goals, essential to a good understanding of the theme, as well as the ability to adapt to technological changes. The evaluation process consists of theoretical tests and practical work also guarantees a correct balance between the efforts dedicated to both components. The objective is to train professionals’ specialized in state-of-the-art techniques and tools but also ensure its ability to follow future developments. In this curriculum unit the problem of algorithms and data structures are presented and evaluated in theoretical component. These concepts are then applied in the resolution of the worksheets and practical work in the context of practical classes Reading: [CLRS] Thomas H. Cormen, Leiserson, C., Rivest, R., & Stein, C. (2009). Introduction to Algorithms, third edition. MIT Press.[KT] Kleinberg, J., & Tardos, E. (2006). Algorithm Design. Pearson Education.[SW] Sedgewick, R., & Wayne, K. (2011). Algorithms, 4th Edition. Pearson Education.[VSC] Vasconcelos, J., & Carvalho, J. V. de. (2005). Algoritmia e Estrutura de Dados: programação nas linguagens C e Java. Editora Centro Atlântico.[AAR2] Rocha, A. A. da. Estruturas de Dados e Algoritmos em Java. FCA - Editora de Informática, ISBN 978-972-722-704-4.[AAR1] Rocha, A. A. da. (2008). Estruturas de Dados e Algoritmos em C. FCA - Editora de Informática