Information updated until 30/06/2026 CODE 80306 ACADEMIC YEAR 2026/2027 CREDITS 6 cfu anno 2 INFORMATICA 11896 (L-31 R) - 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 2025/2026) ALGORITHMS AND DATA STRUCTURES 80298 2025 Computer Science 11896 (coorte 2025/2026) ALGORITHMS AND DATA STRUCTURES 80298 2025 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 Learning classical algorithms and algorithmic patterns by analyzing their correctness and efficiency. Appreciating the potential of randomization in algorithm design through simple examples 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/en/corsi/8759/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Written and oral exam. Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/8759/studenti-disabilita-dsa 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 For further information, please refer to the course’s AulaWeb module or contact the instructor.
