CE 390 | Course Introduction and Application Information - Department of Software Engineering

Course Name

Analysis of Algorithms

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

Prerequisites

CE 221 To succeed (To get a grade of at least DD)

Course Language

English

Course Type

Elective

Course Level

First Cycle

Mode of Delivery

-

Teaching Methods and Techniques of the Course

Problem Solving
Lecture / Presentation

National Occupation Classification

-

Course Coordinator

Prof. Dr. Turhan TUNALI

Course Lecturer(s)

Öğr. Gör. Dr. Aslı ERGÜN

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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to analyze the time and space complexity of algorithms,
2	will be able to efficiently solve suitable problems with greedy algorithms,
3	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,
4	will be able to discuss if a problem could be solved with a dynamic programming algorithm and solve suitable problems with dynamic programming algorithms,
5	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.

Course Description

Greedy algorithms, divideandconquer type of algorithms, dynamic programming and approximation algorithms.

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

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

Course Notes/Textbooks

Introduction to Algorithms, 2/eThomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, ISBN: 9780262533058, MIT PressData Structures and Algorithm Analysis in C++, Mark Allen Weiss, Addision Wesley, Third Edition.

Suggested Readings/Materials

Data Structures and Algorithm Analysis in C++, Mark Allen Weiss, Addision Wesley, Third Edition, 978-0132847377

Algorithm Design. Jon Kleinberg and Eva Tardos. 2006, Pearson Education, ISBN 0321372913

Semester Activities	Number	Weigthing	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	30
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade		60
Weighting of End-of-Semester Activities on the Final Grade		40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	15	4	60
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	4	3	12
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	10	10
Final Exam	1	20	20
		Total	150

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	-	-	X	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	X	-
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	X	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	-	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 390 | Course Introduction and Application Information

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