M.SC. in Computer Engineering (Without Thesis)

CE 601 | Course Introduction and Application Information

Course Name

Advanced Algorithms

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 601	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Third Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Turhan TUNALI
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives

The objective of this course is to introduce algorithms by looking at the real-world problems motivating them. Students will be taught a range of design and analysis techniques for problems that arise in computing applications. Greedy algorithms, divide and conquer type of algorithms and dynamic programming will be discussed within the context of different example applications.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to classify the different types of algorithms together with their purpose of use.
2	will be able to explain time and space complexity of different type of algorithms,
3	will be able to devise efficient greedy algorithms suitable to solve a particular computational problem,
4	will be able to implement efficient divide and conquer algorithms suitable to solve a particular computational problem,
5	will be able to formulate efficient dynamic programs suitable to solve a particular optimization problem.

Course Description

The course covers basics of Algorithms Analysis, graph theoretic concepts, greedy algorithms, divide and conquer algorithms and dynamic programming algorithms.

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction: Some Representative Problems	Course Book; Chapter 1.
2	Basics of Algorithms Analysis	Course Book; Chapter 2.
3	Graphs	Course Book; Chapter 3.
4	Greedy Algorithms: Interval Scheduling	Course Book; Chapter 4.
5	Greedy Algorithms: Scheduling to Minimize Lateness	Course Book; Chapter 4.
6	Greedy Algorithms : Minimum-Cost Arborescences	Course Book; Chapter 4.
7	Divide and Conquer: Counting Inversions	Course Book; Chapter 5.
8	Midterm 1
9	Divide and Conquer: Integer Multiplication	Course Book; Chapter 5.
10	Divide and Conquer: Convolutions and The Fast Fourier Transform	Course Book; Chapter 5.
11	Dynamic Programming: Weighted Interval Scheduling	Course Book; Chapter 6.
12	Dynamic Programming: Subset Sums and Knapsacks	Course Book; Chapter 6.
13	Dynamic Programming: Sequence Alignment	Course Book; Chapter 6.
14	Midterm 2	Course Book; Chapter 11.
15	Final Exam
16	-

Course Notes/Textbooks	Algorithm Design, Jon Kleinberg, Éva Tardos, ISBN-10: 0321295358, ISBN-13: 9780321295354, Addison-Wesley, 2005.
Suggested Readings/Materials	Algorithms, Cormen, T.H., Liesersan, C.E. and Rivest, R.L. ISBN 0-01-013143-0, McGraw-Hill

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	2	40
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

ECTS / WORKLOAD TABLE

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			0
Presentation / Jury			0
Project	1	25	25
Seminar / Workshop			0
Oral Exam			0
Midterms	2	25	50
Final Exam	1	42	42
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	X	-	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	X	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	X	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	X	-	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		X	-	-	-	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		X	-	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		X	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		X	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		X	-	-	-	-