School of Engineering \ Computer Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Computer Engineering
Artificial Intelligence Engineering
Counting methods, discrete probability, basic probability concepts, introduction to random variables and distributions, introduction to algorithms and time complexity analysis, basic sorting algorithms, introduction to graph theory.
Textbook and / or References
Logic in Computer Science Modelling and Reasoning About Systems. By Michael R A Huth and Mark D Ryan. Cambridge University Press.
Concrete Mathematics a Foundation for Computer Science. By Graham, Knuth and Patashnik. Addison-Wesley Publishing Company.
Introduction to Algorithms. Third Edition. By Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. McGraw-Hill.
The purpose of this course is to build a foundation in discrete mathematics and probability theory, covering counting techniques, combinatorics, and random variables, and also introducing basic algorithm analysis and graph theory, so students can model and solve mathematical problems in computer science.
1. Learn counting techniques in discrete mathematics.
2. Learn the fundamental concepts of probability theory.
3. Acquire basic skills in algorithm analysis.
4. Learn the fundamental concepts of graph theory.
Week 1: Introduction to counting problems
Week 2: Permutations and combinations
Week 3: Inclusion-exclusion principle
Week 4: Discrete probability
Week 5: Conditional probability and independence
Week 6: Random variables, expected value, and moments
Week 7: Basic types of discrete random variables
Week 8: Introduction to algorithms and time complexity analysis
Week 9: Asymptotic analysis, solving asymptotic recurrence relations
Week 10: Sorting and searching, heap algorithms
Week 11: Introduction to graph theory
Week 12: Introduction to graph theory
| Tentative Assesment Methods |
| Activities |
Number |
Weight (%) |
| Course Attendance/Participation |
- |
- |
| Laboratory |
- |
- |
| Application |
- |
- |
| Homework |
2 |
10% |
| Project |
- |
- |
| Presentation |
- |
- |
| Field Work |
- |
- |
| Internship |
- |
- |
| Course Boards |
- |
- |
| Quiz |
- |
- |
| Midterm Exam |
1 |
45% |
| Final Exam |
1 |
55% |
|
Total |
110% |
| Tentative ECTS-Workload Table |
| Activities |
Number/Weeks |
Duration (Hours) |
Workload |
| Course Hours (first 6 weeks) |
6 |
4 |
24 |
| Course Hours (last 6 weeks) |
6 |
4 |
24 |
| Laboratory |
- |
- |
- |
| Application |
3 |
2 |
6 |
| Homework |
2 |
20 |
40 |
| Project |
- |
- |
- |
| Presentation |
- |
- |
- |
| Field Work |
- |
- |
- |
| Internship |
- |
- |
- |
| Course Boards |
- |
- |
- |
| Preparation for Quiz |
- |
- |
- |
| Preparation for Midterm Exam |
1 |
20 |
20 |
| Final Exam |
1 |
2 |
2 |
| Preparation for Final Exam |
1 |
30 |
30 |
| Study Hours Out of Class (preliminary work, reinforcement, etc.) |
12 |
2 |
24 |
| Total Workload | | |
170 |
| Total Workload / 30 | | |
170 / 30 |
| | |
5.666667 |
| ECTS Credits of the Course | | |
6 |
|
Program Outcome
**
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2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
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Course Outcome
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| 1 |
A, C
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C
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| 2 |
A, C
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C
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| 3 |
A, C
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C
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| 4 |
A, C
|
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C
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