ALGORITHMS AND DATA STRUCTURES
ALGORITMI E STRUTTURE DATI
|Lecturer||Office hours for students|
|Valerio Freschi||Wednesday 09.00 - 11.00|
|Teaching in foreign languages|
Course with optional materials in a foreign language
This course is entirely taught in Italian. Study materials can be provided in the foreign language and the final exam can be taken in the foreign language.
Assigned to the Degree Course
|Date||Time||Classroom / Location|
|Date||Time||Classroom / Location|
The aim of the course is to illustrate the main techniques for algorithms design and to describe and analyze the most known basic algorithms together with respective used data structures, with particular focus on computational complexity facets.
01. Introduction to algorithms and data structures:
01.01 Algorithms and their typologies
01.02 Correctness of an algorithm with respect to a problem
01.03 Complexity of an algorithm with respect to resource usage
01.04 Data structures and their typologies
02. Classes of problems:
02.01 Decidable and undecidable problems
02.02 Tractable and intractable problems
02.03 Cook theorem
03. Complexity of the algorithms:
03.01 Notations to express the asymptotic complexity
03.02 Computing the complexity of non-recursive algorithms
03.03 Computing the complexity of recursive algorithms
04. Array algorithms:
04.01 Arrays: basic definitions and classical problems
04.02 Traversal algorithm for arrays
04.03 Linear search algorithm for arrays
04.04 Binary search algorithm for sorted arrays
04.05 Comparison criteria for sorting algorithms for arrays
05. List algorithms:
05.01 Lists: basic definitions and classical problems
05.02 Traversal, search, insertion and removal algorithms for lists
05.03 Insertion and removal algorithms for queues
05.04 Insertion and removal algorithms for stacks
06. Tree algorithms:
06.01 Trees: basic definitions and classical problems
06.02 Traversal and search algorithms for binary trees
06.03 Search, insertion and removal algorithms for binary search trees
06.04 Balancing criteria for binary search trees
07. Graph algorithms:
07.01 Graphs: basic definitions and classical problems
07.02 Traversal and search algorithms for graphs
07.03 Topological sorting algorithm for direct acyclic graphs
07.04 Kruskal algorithm
07.05 Prim algorithm
07.06 Properties of the shortest path
07.07 Bellman-Ford algorithm
07.08 Dijkstra algorithm
08. Algorithmic techniques:
08.01 Divide-and-conquer technique
08.02 Dynamic programming
08.03 Greedy technique
08.04 Backtracking technique
09. Laboratory activity:
09.01 C language fundamentals: recall, editing, compilation, debugging
09.02 Pseudorandom number generators: rand and srand functions
09.03 Algorithms complexity experimental evaluation: timing and counters
09.04 Experimental comparison of sorting algorithms for arrays
09.05 Experimental comparison of search algorithms for binary trees
09.06 Implementation of graph algorithms: breadth-first and depth-first traversal algorithms, Dijkstra algorithm
Although there are no mandatory prerequisites for this exam, students are strongly recommended to take it after Procedural Programming and Calculus 1.
It is also worth noticing that the topics covered by this course will be used in Operating Systems, Databases, Object Oriented Programming and Modeling, and Logic and Functional Programming.
Learning Achievements (Dublin Descriptors)
Knowledge and understanding:
At the end of the course, the student will learn: to be aware of the importance of efficient design of algorithms; the fundamental knowledge for analyzing the computational resources required by an algorithm; the main algorithms and data structures for solving basic computational problems.
Applying knowledge and understanding:
The student will learn the methodologies typical of algorithmic design and analysis. In particular she/he will be able to design a series of classic algorithms (sorting, search, etc.) working on different data structures (arrays, lists, trees, graphs) and to analyze their computational complexity. The ability to apply these techniques will be developed and sharpened during laboratory activities, where various algorithms will be analyzed and implementated in C language.
The student will learn how to apply the algorithmic methodologies to understand and solve new problems of computational nature. Critical discussions during classes and laboratory activities will stimulate and develop the making judgements ability of the student.
The student will acquire the ability to communicate the fundamental concepts of algorithms and data structures with an appropriate and rigorous terminology. She/he will learn to describe the problems related to the design and analysis of efficient algorithms, and the methodologies adopted to solve them.
The student will acquire the ability to study and learn algoritmic techniques and fundamental data structures. She/he will learn to recognize the importance of computational resources (in particular space and time) in order to be able to autonomoulsy develop solutions for novel problems related to efficient program design.
The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it
Didactics, Attendance, Course Books and Assessment
Theory lectures and laboratory exercises, both face-to face and on-line.
Although recommended, course attendance is not mandatory.
- Course books
Cormen, Leiserson, Rivest, Stein, "Introduzione agli Algoritmi e alle Strutture Dati", McGraw-Hill, 2010.
(Cormen, Leiserson, Rivest, Stein, "Introduction to Algorithms", MIT Press, 2009.)
Demetrescu, Finocchi, Italiano, "Algoritmi e Strutture Dati", McGraw-Hill, 2008.
Crescenzi, Gambosi, Grossi, "Strutture di Dati e Algoritmi", Pearson/Addison-Wesley, 2012.
Sedgewick, "Algoritmi in C", Pearson, 2015.
(Sedgewick, "Algorithms in C", Addison-Wesley, 1998.)
Learning achievements will be evaluated through three types of assessments: project (to be developed individually or by groups of two students), written exam, and oral exam. This procedure is aimed in particular at evaluating the achievement of applying knowledge and understanding, with respect to problem solving and efficient usage of computational resources and, at the same time, at assessing the student's capabilities of summarizing concepts and her/his communication skills.
The individual project, which changes at each exam session, has to be submitted at least seven days before the written exam. The project is passed if the mark (which is valid for all the exam calls of the same academic year) is at least 18/30. Should the project be resubmitted in a subsequent exam call, the mark of the previously submitted project is cancelled. If the resubmission takes place in the same exam session, a 5/30 penalty is applied to the mark of the newly submitted project.
The written exam, which can be taken only if the project has been passed, consists of seven questions to be answered in one hour and is passed if the mark (which is valid only for the exam call in which the written exam is taken) is at least 18/30.
The oral exam, which can be taken only if the project and the written exam have been passed, consists of further questions about the course program. If passed, it determines a spread between -5/30 and 5/30 of the average of the two previous marks, thus yielding the final mark.
The course offers additional e-learning facilities on the Moodle platform > elearning.uniurb.it
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