CE 215 | Course Introduction and Application Information

Course Name
Discrete Structures in Computer Science
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 215
Fall
3
0
3
6

Prerequisites
None
Course Language
English
Course Type
Required
Course Level
First Cycle
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course seeks to place on solid foundations the most common structures of computer science, to illustrate proof techniques, to provide the background for an introductory course in computational theory, and to introduce basic concepts of probability theory.
Course Description The students who succeeded in this course;
  • wii be able to state a logical argument,
  • will be able to practically use fundamental mathematical notation and concepts,
  • will be able to practise basic concepts of mathematical proof (direct proof, proof by contradiction, mathematical induction),
  • will be able to solve elementary combinatorial and counting problems,
  • will be able to identify the relations between sets and the properties of these relations,
Course Content Topics include Boolean algebras, logic, set theory, relations and functions, graph theory, counting, combinatorics, and basic probability theory.

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Logic: Propositional Logic Rosen, Discrete Mathematics and Its Applications, Chapter 1, Sections 1.1 - 1.3
2 Logic: Predicate Logic Rosen, Discrete Mathematics and Its Applications, Chapter 1, Sections 1.4, 1.5
3 Logic: Logic and Proofs Rosen, Discrete Mathematics and Its Applications, Chapter 1, Sections 1.6, 1.8, 1.9
4 Sets, Functions Rosen, Discrete Mathematics and Its Applications, Chapter 2, Sections 2.1-2.3
5 Sequences and Sums Rosen, Discrete Mathematics and Its Applications, Chapter 2, Section 2.4, 2.5
6 Number Theory: Divisibility Rosen, Discrete Mathematics and Its Applications, Chapter 4, Sections 4.1, 4.2
7 Midterm Review
8 MIDTERM
9 Number Theory: Primes Rosen, Discrete Mathematics and Its Applications, Chapter 4, Sections 4.3-4.5
10 Mathematical Induction Rosen, Discrete Mathematics and Its Applications, Chapter 5, Sections 5.1, 5.2
11 Counting Rosen, Discrete Mathematics and Its Applications, Chapter 6, Sections 6.1-6.4, Chapter 8, Section 8.5
12 Discrete Probability Rosen, Discrete Mathematics and Its Applications, Chapter 7
13 Relations Rosen, Discrete Mathematics and Its Applications, Chapter 9, Sections 9.1, 9.3, 9.5, 9.6
14 Coding Theory Rosen, Discrete Mathematics and Its Applications, Chapter 12, Section 12.6
15 Graphs & Trees Rosen, Discrete Mathematics and Its Applications, Chapter 10, Sections 10.1-10.3, Chapter 11, 11.1, 11.2
16 Semester Review

 

Course Notes/Textbooks

Discrete Mathematics and Its Applications, Kenneth H. Rosen, 7th edition, McGraw Hill, 2013

Suggested Readings/Materials

Discrete and combinatorial mathematics: an applied introduction. R.P. Grimaldi. Fifth Edition. ISBN: 0321211030

Discrete Mathematics for Computer Scientists, J.K. Truss, 2nd edition, Pearson, 1999

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
10
25
Presentation / Jury
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral Exam
1
45
Total

Weighting of Semester Activities on the Final Grade
11
55
Weighting of End-of-Semester Activities on the Final Grade
1
45
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
Including exam week: 16 x total hours
16
3
48
Laboratory / Application Hours
Including exam week: 16 x total hours
16
Study Hours Out of Class
16
4
Field Work
Quizzes / Studio Critiques
Homework / Assignments
10
3
Presentation / Jury
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
14
Final / Oral Exam
1
24
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems X
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose X
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice X
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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