OVERVIEW

The course of Algorithms and Data Structures aims at expanding the students' knowledge and skills related to programming in the small with imperative languages; it provides the basis for designing efficient algorithms and for developing data structures that enable effective information organization.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at improving knowledge and skills of programming in the small, through imperative languages, providing the basics for designing correct and efficient algorithms, and for developing data structures that enable effective and efficient organization of data.

AIMS AND LEARNING OUTCOMES

At the end of the course, the student will be able to:

- compute the complexity of known algorithms (sorting, adding, searching and modifying elements in a data structure) in order to identify the most efficient one

- design the interface of a data type

- implement the data type with different data structures that include indexed and linked structures

- understand the difference in the efficiency of the functions supported by the data type, when different data structures are employed

TEACHING METHODS

Traditional, with frontal lessons and laboratory sessions

SYLLABUS/CONTENT

Methods for algorithm analysis: cost criteria, asymptotic notation, complexity analysis of recursive algorithms. Examples of development and analysis of algorithms.
Sorting algorithms: insertion sort, selection sort, bubble sort, mergesort, quicksort
Basic data structures: arrays and lists; stacks and queues; dictionaries implemented with lists.
Dictionaries: implementation with binary search trees and hash tables.
Trees: indexed and linked representations for binary trees and general trees; depth-first search and breadth-first search of trees.
Search Trees: Binary search trees, search trees as a data structure for implementing dictionaries, balanced trees.
Hash tables: collision lists and open addressing.
Priority queues: implementation with lists and heaps.
Graphs: definitions, data structures, primitives for querying and updating graphs; graph visits in depth and in width; examples of application of a graph visit algorithms.
Laboratory: C++ laboratories related to course topics

RECOMMENDED READING/BIBLIOGRAPHY

All topics covered by the program are faced during the frontal lessons. The teaching material provided by the teachers via AulaWeb (including the fragments of C++ code implementing the algorithms and data structures addressed during the course) and notes taken during classroom lessons are essential for preparing the exam.

TEACHERS AND EXAM BOARD

Exam Board

VIVIANA MASCARDI (President)

ARNAUD HENRI PAUL SANGNIER

MAURIZIO LEOTTA (President Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Exam description

The exam consists of a written part and a laboratory part. The two parts are independent of each other: students can book and perform only the written test in one exam session and book and perform the lab test in another session, and vice versa. It is not necessary to pass one of the two tests to be admitted to the other. The written part consists of an initial quiz that represents a "barrier": if the student does not reach a threshold on the questions in that initial part, the student can face neither the written part, not the laboratory session. The "barrier" quiz can be faced in any of the five available appeals.

Computation of the final mark

The final mark is obtained as the sum of the written mark + the lab mark + the mark of the exercises evaluated during the year. This sum is rounded to the nearest integer. 

ASSESSMENT METHODS

The various parts of the exam have been carefully designed by the teachers to verify whether the student is able to

- compute the complexity of known algorithms (sorting, adding, searching and modifying elements in a data structure) in order to identify the most efficient one

- design the interface of a data type

- implement the data type with different data structures that include indexed and linked structures

- understand the difference in the efficiency of the functions supported by the data type, when different data structures are employed

Details on how to prepare for the exam and the degree of understanding of each topic delat with during the course will be given during the lessons. Examples of previous exam texts will be made available to allow students to understand how the acquisition of the required skills is assessed.

Exam schedule