Basic Information

Course Code:

CS 2007

Course Name:

Discrete Computational Structures II

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

4.00

Instructor:

Dr. Öğr. Üyesi Aslı BAY

Course Objectives

The main objective of this course is to provide the students with a knowledge on theoretical foundations of problem solving and experience in working with discrete computational structures common in computer science and computational problems.

Course Content

Discussion of counting principles including permutations and combinations defined over sets/multisets. Basic logic, sets, induction, recursion, relations, functions and discrete probabilities are discussed. Introduction to graph theory including basic definitions in graph theory, common classes of graphs and fundamental theorems. Discussion on well-known problems in graph theory and related algorithms including shortest-path problem, weighted graphs and Dijkstra's algorithm, and spanning tree algorithms.

Prerequisites / Corequisites

None.

Course Books / Materials / Recommended Resources

Modern Graph Theory, Bela Bollobas, Springer, ISBN: 978-1-4612-0619-4

Learning Outcomes

Code	Description
LO1	Use counting principles to solve combinatorial problems.
LO2	Prove fundamental propositions in graph theory
LO3	Sets, relations, recursion, functions, and discrete probabilities
LO4	Analyze graph algorithms and apply them on computer science related problems.

Weekly Course Content

Week	Content
1
2
3
4
5

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	14	3.00	42.00
Pre-Class Individual Study	14	2.00	28.00
Midterm Exam/Preparation	1	10.50	10.50
Final Exam/Preparation	1	12.00	12.00
Homework	2	8.50	17.00
Other	1	10.50	10.50
Total Workload (Hours)			120
ECTS Credit (Workload / 25)			4

Assessment

#	Assessment Type	Contribution (%)
1	Final Exam	%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Lecture (expository teaching), interactive discussion	Listening and taking notes.	Standard classroom technologies, multimedia tools (projector, computer, digital presentations)
2	Problem Solving	* Analyzing physical and physiological problems using problem-solving techniques and developing appropriate solutions.
3	Brainstorming	The purpose of forming pairs is to ensure that they are open to innovation during the idea generation phase	Standard classroom technologies, multimedia tools, projector, computer, overhead projector
4	Homework	Solving and analyzing homework questions using a computer and Excel.	Computer

Academic Integrity and Artificial Intelligence

Students are expected to comply with the principles of academic integrity within the scope of this course. Cheating, plagiarism, or presenting someone else’s work as one’s own is against academic ethical rules. Artificial intelligence tools may only be used for supportive purposes and should be evaluated in a way that supports the student’s own learning process.

Sustainable Development Goals