Computer Engineering Department

Algorithms And Data Structures(INF224-A)

Content
Course Code Course Name Semester Theory Practice Lab Credit ECTS
INF224-A Algorithms And Data Structures 3 2 0 2 3 5
Prerequisites INF112/INF114
Admission Requirements INF112/INF114
Language of Instruction French
Course Type Compulsory
Course Level Bachelor Degree
Course Instructor(s) Günce Keziban ORMAN korman@gsu.edu.tr (Email)
Assistant Musa Şervan ŞAHİN mssahin@gsu.edu.tr (Email)
Objective The aim of the course is to equip student with skills of choosing the most appropriate data structure, using these data structures into different types of algorithms, analyzing the performance of proposed algorithms and choosing the most efficient one and programming these algorithms in C.
Content Week 1 — Introduction to Data Structures and Algorithms; C language review
Week 2 — Arrays, pointers, linked lists, queues, stacks, recursive calls
Week 3 — Linked lists; complexity, Big-O notation, running time, computability
Week 4 — Search Methods and Trees 1: sequential search, binary search
Week 5 — Search Methods and Trees 2: Red-Black Trees, AVL trees, n-ary trees
Week 6 — Sorting Algorithms: Bubble sort, Quick sort, Insertion sort, Merge sort
Week 7 — Heap sort and heap trees; Bucket/Radix sort; hash tables; Huffman coding
Week 8 — Midterm Exam
Week 9 — Graphs: multidimensional arrays, graphs with pointers, undirected and directed graphs
Week 10 — Graphs: graph traversal (DFS, BFS), Kruskal, Prim, and Dijkstra algorithms
Week 11 — Dynamic Programming 1: Bellman–Ford and Floyd–Warshall algorithms
Week 12 — Matching Algorithms
Week 13 — End-of-term research presentations / individual performance evaluation
Week 14 — End-of-term research presentations / individual performance evaluation
Course Learning Outcomes At the end of this course, students obtain the following information:
• Having deep knowledge on each data structure.
• Choosing the most appropriate data structure in respect to the available data.
• Using the data structures in different algorithms.
• Analyzing the performance of algorithms.
Teaching and Learning Methods Lecture, Discussion, Demonstration, Case Study, Problem Solving, Cooperative Learning, Project, Brainstorming
References 1. M.A. Weiss, Data Structures & Algorithm Analysis in C++, 1999, Addison Wesley.
2. A.M. Tanenbaum, Data Structures using C, 1989, Prentice Hall.
3. A. Drozdek, Data Structures and Algorithmss in C++, 2004, Course Technology.
4. R. Sedgewick, Algorithms in C, Parts 1-4: Fundamentals, Data Structures, Sorting, Searching, 1997, Addison-Wesley.
5. Olcay Taner Yıldız, C && Java ile Veri Yapılarına Giriş, Boğaziçi Üniversitesi Yayınevi, 2013.
Print the course contents
Weekly Contents
Theory Topics
Week Weekly Contents
1 Introduction to Data Structures and Algorithms; C language review
2 Arrays, pointers, linked lists, queues, stacks, recursive calls
3 Linked lists; complexity, Big-O notation, running time, computability
4 Search Methods and Trees 1: sequential search, binary search
5 Search Methods and Trees 2: Red-Black Trees, AVL trees, n-ary trees
6 Sorting Algorithms: Bubble sort, Quick sort, Insertion sort, Merge sort
7 Heap sort and heap trees; Bucket/Radix sort; hash tables; Huffman coding
8 Midterm Exam
9 Graphs: multidimensional arrays, graphs with pointers, undirected and directed graphs
10 Graphs: graph traversal (DFS, BFS), Kruskal, Prim, and Dijkstra algorithms
11 Dynamic Programming 1: Bellman–Ford and Floyd–Warshall algorithms
12 Matching Algorithms
13 End-of-term research presentations / individual performance evaluation
14 End-of-term research presentations / individual performance evaluation
Practice Topics
Week Weekly Contents
1 Introduction to Data Structures and Algorithms; C language review
2 Arrays, pointers, linked lists, queues, stacks, recursive calls
3 Linked lists; complexity, Big-O notation, running time, computability
4 Search Methods and Trees 1: sequential search, binary search
5 Search Methods and Trees 2: Red-Black Trees, AVL trees, n-ary trees
6 Sorting Algorithms: Bubble sort, Quick sort, Insertion sort, Merge sort
7 Heap sort and heap trees; Bucket/Radix sort; hash tables; Huffman coding
8 Midterm Exam
9 Graphs: multidimensional arrays, graphs with pointers, undirected and directed graphs
10 Graphs: graph traversal (DFS, BFS), Kruskal, Prim, and Dijkstra algorithms
11 Dynamic Programming 1: Bellman–Ford and Floyd–Warshall algorithms
12 Matching Algorithms
13 End-of-term research presentations / individual performance evaluation
14 End-of-term research presentations / individual performance evaluation
Assessment System
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 14 60
Contribution of final exam to overall grade 1 40
Toplam 15 100
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 1 20
Project 1 25
Laboratory 10 5
Other Applications 0 0
Quiz 2 10
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Make-up 0 0
Toplam 14 60
Relation of Proficiency
No Program Learning Outcomes Contribution
1 2 3 4 5
1 Matematik, fizik ve mühendislik bilimlerine özgü konularda yeterli bilgi birikimi; bu alanlardaki kuramsal ve uygulamalı bilgileri, mühendislik problemlerinin modellenmesi ve çözümünde kullanabilme becerisi. X
2 Karmaşık bilgisayar mühendisliği problemlerini saptama, tanımlama, formüle etme ve çözme becerisi; bu amaçla uygun analiz ve modelleme yöntemlerini seçme ve uygulama becerisi. X
3 Yazılımsal veya donanımsal karmaşık bir sistemi, süreci veya donanımı gerçekçi kısıtlar ve koşullar altında, belirli gereksinimleri karşılayacak şekilde tasarlama becerisi; bu amaçla modern tasarım yöntemlerini uygulama becerisi. X
4 Mühendislik uygulamalarında karşılaşılan karmaşık problemlerin analizi ve çözümü için gerekli olan modern teknik ve araçları geliştirme, seçme ve kullanma becerisi; bilişim teknolojilerini etkin bir şekilde kullanma becerisi. X
5 Analitik düşünce ile bir sistemi, sistem bileşenini ya da süreci analiz etme, modelleme, deney tasarlama ve yapma, veri toplama, çözüm algoritmaları üretebilme, uygulamaya alma ve geliştirme becerileri. X
6 Disiplin içi ve çok disiplinli takımlarda etkin biçimde çalışabilme becerisi; bireysel çalışma becerisi. X
7 Türkçe sözlü ve yazılı etkin iletişim kurma becerisi; en az iki yabancı dil bilgisi; etkin rapor yazma ve yazılı raporları anlama, yazılım ve donanım tasarımını, gerekirse teknik resim metotları kullanarak raporlayabilme, etkin sunum yapabilme becerisi. X
8 Bilgiye erişebilme ve bu amaçla kaynak araştırması yapabilme, veri tabanları ve diğer bilgi kaynaklarını kullanabilme becerisi X
9 Yaşam boyu öğrenmenin gerekliliği bilinci; kendini sürekli yenileme becerisi. X
10 Mesleki etik ilkelerine uygun davranma, mesleki sorumluluk bilinci; mühendislik uygulamalarında kullanılan standartlar hakkında bilgi. X
11 Proje yönetimi, risk yönetimi ve değişiklik yönetimi gibi, iş hayatındaki uygulamalar hakkında bilgi; girişimcilik, yenilikçilik hakkında farkındalık; sürdürülebilir kalkınma hakkında bilgi. X
12 Mühendislik uygulamalarının evrensel ve toplumsal boyutlarda sağlık, çevre ve güvenlik üzerindeki etkileri ve çağın mühendislik alanına yansıyan sorunları hakkında bilgi.
13 Bilgisayar mühendisliği uygulamalarının hukuki ve etik boyutları konusunda farkındalık.
ECTS
Activities Number Period Total Workload
Class Hours 14 3 42
Working Hours out of Class 14 2 28
Assignments 3 4 12
Presentation 0 0 0
Midterm Examinations (including preparation) 1 6 6
Project 0 0 0
Laboratory 2 14 28
Other Applications 0 0 0
Final Examinations (including preparation) 1 10 10
Quiz 0 0 0
Term Paper/ Project 0 0 0
Portfolio Study 0 0 0
Reports 0 0 0
Learning Diary 0 0 0
Thesis/ Project 0 0 0
Seminar 0 0 0
Other 0 0 0
Make-up 0 0 0
Yıl Sonu 0 0 0
Hazırlık Yıl Sonu 0 0 0
Hazırlık Bütünleme 0 0 0
Total Workload 126
Total Workload / 25 5.04
Credits ECTS 5
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