Department of Computer Engineering

CE 390 | Course Introduction and Application Information

Course Name

Analysis of Algorithms

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 390	FALL	3	0	3	5

Prerequisites	To successfully complete CE 221 (with a grade of at least DD)
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-to-face, Online
Teaching Methods and Techniques of the Course	Problem solving Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Prof. Dr. Turhan Tunalı
Course Lecturer(s)	Prof. Dr. Turhan Tunalı
Assistant(s)	-

Course Objectives

The objective of this course is to introduce algorithms by looking at the realworld problems motivating them. Students will be taught a range of design and analysis techniques for problems that arise in computing applications. Greedy algorithms, divideandconquer type of algorithms, and dynamic programming will be discussed within the context of different example applications. Approximation algorithms with an emphasis on load balancing and set cover problems will also be covered.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	will be able to analyze the time and space complexity of algorithms.					X
LO2	will be able to efficiently solve suitable problems with greedy algorithms.					X
LO3	will be able to discuss if a problem could be solved with divide and conquer algorithm and solve suitable problems with divide and conquer algorithm.					X
LO4	will be able to discuss if a problem could be solved with a dynamic programming algorithm and solve suitable problems with dynamic programming algorithms.					X
LO5	will be able to compare the trade-off between the time complexity and the optimality of the solution to find the most optimal solution and discuss approximation algorithms when the optimal is not feasible.					X

Course Description

Greedy algorithms, divide-and-conquer type of algorithms, dynamic programming and approximation algorithms.

Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction and motivation. Mathematical foundations, summation, recurrences and growth of functions	Cormen Chapter 2, 3, and 4	LO1
2	Asymptotic notation and Master theorem	Cormen Chapter 4	LO1
3	Binary heaps and analysis of heapsort	Cormen Chapter 6	LO1
4	Sorting theory and more comparison sorting algorithms: Analysis of merge sort and Quicksort.	Cormen Chapter 7	LO3
5	Worst case analysis of Quicksort	Cormen Chapter 7	LO1
6	Sorting in linear time, lower bounds for sorting, counting sort, radix sort, bucket sort	Cormen Chapter 8	LO2
7	Medians and order statistics. Finding median and rank in linear time, selection algorithm.	Cormen Chapter 9	LO1
8	Midterm		-
9	Elementary data structures and runtime analysis of insertion, deletion and update	Cormen Chapter 10	LO1
10	Hash tables and runtime analysis.	Cormen Chapter 11	LO1
11	Binary search trees and Redblack trees	Cormen Chapter 12 and 13	LO3
12	Btrees and Augmenting data structures	Cormen Chapter 18	LO1
13	Amortized analysis	Cormen Chapter 17	LO5
14	Binomial heaps and fibonacci heaps	Cormen Chapter 19 and 20	LO4
15	Semester Review		-
16	Final Exam		-

Course Notes/Textbooks	Thomas H. Cormen; Charles E. Leiserson; Ronald L. Rivest; and Clifford Stein. 2009. Introduction to Algorithms; Third Edition (3rd. ed.). The MIT Press.
Suggested Readings/Materials	-

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	1	30	X				X
Midterm	1	30	X	X	X	X
Final Exam	1	40	X	X	X	X	X
Total	3	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	15	4	60
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	4	3	12
Presentation / Jury	-	-	-
Project	-	-	-
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	10	10
Final Exam	1	20	20
		Total	150

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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