| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Describe the historical development and fundamental concepts of artificial intelligence applications.
- Know the basic concepts and components related to propositional logic and first-order logic, and apply them to problem-solving.
- Understand the fundamental concepts of knowledge representation and expert systems.
- Become familiar with approaches and algorithms used in searching and problem solving.
- Gain general knowledge of neural networks, machine learning, data mining, and deep learning.
- Understand and interpret value creation and competitive advantage models.
- Utilize artificial intelligence applications to improve organizational efficiency and enhance a business's competitive advantage.
- Design, define, and present an artificial intelligence application project for a business as part of a term project.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course aims to equip students with knowledge and skills related to artificial intelligence applications in businesses. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to artificial intelligence, its history, and key concepts |
|
2nd Week |
Propositional logic, first-order logic |
|
3rd Week |
Knowledge representation |
|
4th Week |
Reasoning under uncertainty, decision analysis, expert systems |
|
5th Week |
Searching and problem solving |
|
6th Week |
Introduction to advanced topics: neural networks, machine learning, data mining, deep learning |
|
7th Week |
Introduction to advanced topics: neural networks, machine learning, data mining, deep learning |
|
8th Week |
Midterm exam |
|
9th Week |
Value creation and competitive advantage models |
|
10th Week |
Impact of artificial intelligence on organizational activities and competitiveness |
|
11th Week |
Value creation models in companies |
|
12th Week |
General review |
|
13th Week |
Project presentations and evaluation |
|
14th Week |
Project presentations and evaluation |
|
| Recommended or Required Reading | Neapolitan, R. E., & Jiang, X. (2018). Artificial intelligence: With an introduction to machine learning. CRC press.
Wodecki, A. (2019). Artificial Intelligence in Value Creation. Palgrave Macmillan.
Ertel, W. (2017). Introduction to Artificial Intelligence. Springer-Verlag London Limited.
Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
40 |
| Project |
1 |
20 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
1 |
14 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
22 |
22 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
|
|
|
| Presentation (including preperation) |
|
|
|
| Project |
1 |
24 |
24 |
| Report writing |
1 |
23 |
23 |
| Total Work Load | 152 |
| Total Work Load / 30 (h) | 5.06 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
