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Information
| Code | BMS429 |
| Name | Artificial Intelligence Systems |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MUTLU AVCI |
Course Goal
Learning the basic artificial intelligence techniques and understanding implementation of artificial intelligence on engineering problems.
Course Content
Fundamentals of artificial intelligence, regression techniques, classification techniques, learning algorithms, artificial neural networks, genetc algorithm, decision trees, fuzzy logic, support vector machines
Course Precondition
No prerequisite
Resources
Lecture Notes and slides are available.
Notes
Vasif V. Nabiyev, Artificial Intelligence, Seckin Publication, 2005.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Recognize smart and intelligent systems. |
| LO02 | Explain the learning algorithms. |
| LO03 | Know the regression and classification concepts. |
| LO04 | Capable of training artificial neural networks. |
| LO05 | Explain the genetic algorithm. |
| LO06 | Capable of implementing decision trees. |
| LO07 | Knows and uses fuzzy logic. |
| LO08 | Know support vector machines. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Scientific problems encountered in the field of medicine and medical technologies; the ability to solve problems by applying the technical approaches of mathematics, science and engineering sciences. | 5 |
| PLO02 | Yetkinlikler - Öğrenme Yetkinliği | To be able to improve oneself by embracing the importance of lifelong learning and by following the developments in science-technology and contemporary issues. | |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Assess the contributions of engineering solutions on medicine, medical technologies and healthcare. | |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Identifying problems related to biomedical engineering. | 5 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Modeling problems related to biomedical engineering. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Analyzing data and interpreting the results. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | To be able to use modern techniques and computational tools required for engineering applications. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Ability to analyze and design a process in line with a defined goal. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to understand the problems and wishes of the medical doctor in their scientific studies from an engineering point of view. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Expressing ideas verbally and in writing, clearly and concisely. | |
| PLO11 | Yetkinlikler - Alana Özgü Yetkinlik | To be conscious of calibration and quality assurance systems in Biomedical Engineering. | 3 |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | Design and Implement Experiments. | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to act independently, set priorities and creativity. | |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Being aware of national and international contemporary issues in the field of Biomedical Engineering. | |
| PLO15 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Ability to work in interdisciplinary teams. | |
| PLO16 | Yetkinlikler - Alana Özgü Yetkinlik | To have a sense of professional and ethical responsibility. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to artificial intelligence | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 2 | Error minimization and regression | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 3 | Artificial neural networks and learning algorithms | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 4 | Error backpropagation learning | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 5 | Multi Layer Perceptron ANN | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 6 | Radial Basis Function ANN | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 7 | General regression neural network | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Reading lecture materials | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Probabilistic neural network | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 10 | Genetic algorithm | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 11 | Decision trees | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 12 | Fuzzy logic | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 13 | Support vector machines 1 | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 14 | Support vector machines 2 | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 15 | Self Orginizing Map | Reading lecture materials | Ölçme Yöntemleri: Yazılı Sınav |
| 16 | Term Exams | Test and classical mixed exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Test and classical mixed exam | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 128 | ||
| Total Workload / 25 (h) | 5,12 | ||
| ECTS | 5 ECTS | ||