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Information
| Unit | FACULTY OF ENGINEERING |
| BIOMEDICAL ENGINEERING PR. | |
| Code | BMS429 |
| Name | Artificial Intelligence Systems |
| Term | 2018-2019 Academic Year |
| 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 |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MUTLU AVCI |
| Course Instructor |
Prof. Dr. MUTLU AVCI
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
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
Resources
Notes
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 | Recognize fuzzy logic. |
| LO08 | Know support vector machines. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | 1. Solve the scientific problems encountered in medicine and medical technologies by applying technical approaches of disciplines. 2. Self development on science and technology issues. 3. Assess the contributions of engineering solutions on medicine, medical technologies and healthcare | 3 |
| PLO02 | - | 1. Define the problems about Biomedical Engineering 2. Modelling the problems about Biomedical Engineering. | 3 |
| PLO03 | - | 1. Analyse data and interpret results | 4 |
| PLO04 | - | 1. Utilize modern techniques and computing tools which are essential for Engineering applications | 4 |
| PLO05 | - | 1. Design and analyse a defined process 2. Recognise national and international problems for Biomedical Engineering | 4 |
| PLO06 | - | Understand the research problems of medical doctor with engineering perspective | 5 |
| PLO07 | - | 1. Describe the ideas clearlywith written and verbally 2. Have the interdisciplinary teamwork skills | 5 |
| PLO08 | - | 1. Have knowledge on calibration and quality assurance systems in Biomedical Engineering 2. Have the sense of responsibility and professional ethics | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to artificial intelligence | Reading lecture materials | |
| 2 | Error minimization and regression | Reading lecture materials | |
| 3 | Artificial neural networks and learning algorithms | Reading lecture materials | |
| 4 | Error backpropagation learning | Reading lecture materials | |
| 5 | Multi Layer Perceptron ANN | Reading lecture materials | |
| 6 | Radial Basis Function ANN | Reading lecture materials | |
| 7 | General regression neural network | Reading lecture materials | |
| 8 | Mid-Term Exam | Reading lecture materials | |
| 9 | Probabilistic neural network | Reading lecture materials | |
| 10 | Genetic algorithm | Reading lecture materials | |
| 11 | Decision trees | Reading lecture materials | |
| 12 | Fuzzy logic | Reading lecture materials | |
| 13 | Support vector machines 1 | Reading lecture materials | |
| 14 | Support vector machines 2 | Reading lecture materials | |
| 15 | Self Orginizing Map | Reading lecture materials | |
| 16 | Term Exams | Test and classical mixed exam | |
| 17 | Term Exams | Test and classical mixed exam |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | ||