Information Package / Course Catalogue
| Course Code | : BPR210 |
| Course Type | : Required |
| Couse Group | : Short Cycle (Associate's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 1 |
| Prt. | : 1 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 4 |
The aim of this course is to teach and apply the methods, application languages and search paradigms in the field of artificial intelligence effectively; In this way, students should be able to increase their analytical and theoretical power and solve problems effectively.
Artificial Intelligence (AI) has been a field that has a wide range of applications over time. AI systems are now able to understand conversations, play chess and do household chores. In this course, how to present information about artificial intelligence systems; how the action can be divided into effective sections and how the best (optimal) result or the almost-best result can be found among the possibilities. It will also be discussed how to deal with the unknowns in the world, how to learn from the experience and how to decide from the data.
| Ins. Mehmet Can HANAYLI |
| 1. | To learn artificial intelligence methods and applications in daily life. |
| 2. | To be able to learn and apply necessary paradigms of paradigm to solve mathematical problems such as constraints |
| 3. | To be able to use the appropriate search paradigm to solve the problem and to produce a solution to the problem. |
| 4. | To be able to comprehend learning paradigms. |
| 5. | Ability to analyze artificial intelligence based programming with modern programming languages (Java, C, C ++, C #, etc.). |
| 1. | Russell, S.J. And Norvig, P., “Artificial Intelligence : A Modern Approach”, Third Edition, Prentice-Hall, 2009. (AIMA |
| 2. | Yapay Zeka Geçmişi ve Geleceği Nils J. Nilsson (Eser Sahibi), Mehmet Doğan (Çevirmen) Boğaziçi Yayınları; 1. baskı (6 Şubat 2019) |
| 3. | Decision Support Systems (Data Warehouse - Data Mining - Clinical KDS) |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %40 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 0 | 1 | 14 |
| Lecture - Practice | 14 | 0 | 1 | 14 |
| Assignment | 8 | 4 | 0 | 32 |
| Project | 4 | 3 | 4 | 28 |
| Midterm Examination | 1 | 5 | 1 | 6 |
| Final Examination | 1 | 5 | 1 | 6 |
| TOTAL WORKLOAD (hours) | 100 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | |
OÇ-1 | 5 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
OÇ-2 | 4 | 4 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 3 | 3 |
OÇ-3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
OÇ-4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
OÇ-5 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 4 | 4 |