CODE 80306 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 2 INFORMATICA 8759 (L-31) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester PREREQUISITES Propedeuticità in ingresso Per sostenere l'esame di questo insegnamento è necessario aver sostenuto i seguenti esami: Computer Science 8759 (coorte 2024/2025) ALGORITHMS AND DATA STRUCTURES 80298 2024 TEACHING MATERIALS AULAWEB OVERVIEW The aim of the course is to learn classical data structures and algorithms, and to analyze their correctness and efficiency. Base notions on complexity and elementary data structures are assumed as prerequisites. Since the course is given in italian, please have a look at the italian version of this page for further information. AIMS AND CONTENT LEARNING OUTCOMES To learn classical data structures and algorithms, and to be able to analyze their correctness and efficiency. Base notions on complexity and elementary data structures are assumed as prerequisites. Topics include design and analisys techniques, sorting algorithms, advanced data structures, graph algorithms, NP-completeness. AIMS AND LEARNING OUTCOMES At the end of the course the student will be able to: Know classical algorithms and algorithmic schemes. Analyze correctness and efficiency of an algorithm. Appreciate the potential of randomization in algorithm design through simple examples. PREREQUISITES Basic knowledge of algorithms and data structures. TEACHING METHODS Traditional. SYLLABUS/CONTENT Design and analysis techniques, asymptotic notations, correctness and complexity of recursive and iterative algorithms, divide-et-impera, dynamic programming, greedy algoritms. Sorting: simple sorts, mergesort, quicksort, heapsort, lower bound for comparison-based sorting algorithms, linear sorts. Advanced data structures: heaps, union-find structures. Graphs: definitions, representations, visits, topological sorting, strongly-connected components, single-source shortest paths (Dijkstra algorithm), minimum spanning tree (Prim and Kruskal algorithms). Theory of NP-completeness: complexity classes, NP-complete problems, approximation algorithms RECOMMENDED READING/BIBLIOGRAPHY Lecture notes. Introduction to algorithms and data structures. Cormen, Leiserson, Rivest, Stein. McGraw Hill. TEACHERS AND EXAM BOARD ALESSANDRO VERRI Ricevimento: Appointment by email ENRICO PUPPO Ricevimento: Appointment by email: enrico.puppo@unige.it During class period appointments for groups can be set by posting on the course forum on AulaWeb. LESSONS LESSONS START According to the calendar approved by the Degree Program Board: https://corsi.unige.it/corsi/8759/studenti-orario Class schedule ALGORITHM ANALYSIS AND DESIGN EXAMS EXAM DESCRIPTION Written and oral exam. ASSESSMENT METHODS The written exam checks the ability of the student to apply in practice learned notions. The oral exam checks the understanding of concepts and the ability to appropriately illustrate learned notions. FURTHER INFORMATION Contact the instructor for any additional information not included in the course description.
