Information Package / Course Catalogue
| Course Code | : MME547 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 8 |
Artificial intelligence (AI) affects and changes various aspects of society and its activities. Healthcare is also at the beginning of this transformation. The potential use of AI technologies in healthcare needs healthcare professionals with knowledge of AI to enable interactive and explanatory AI and ensure the quality of AI-based systems to increase patient safety. Knowledge of AI is also important for people involved in decision-making, procurement and implementation of AI-based systems. The course introduces and provides basic knowledge about artificial intelligence (AI) and its application in health care.
In this course, students will learn about the basics of AI and its application in healthcare such as medical image analysis, data analysis and data extraction, natural language processing and decision support systems. The course will also address ethical issues and data protection issues, regulations and entrepreneurship aspects of AI in healthcare.
| Prof. Pınar DEMİRCİOĞLU |
| 1. | Each student will be able to define AI and discuss what AI can and cannot do |
| 2. | Each student will be able to identify various AI applications in medicine and explain how they are transforming healthcare |
| 3. | Each student will be able to recognize the power of big data in enabling AI and describe the different types of data representations |
| 4. | Each student will be able to recognize the power of AI algorithms in solving medical problems and discuss how they can be applied in the medical field |
| 5. | Each student will be able to apply some AI techniques to solve real-world medical problems |
| 1. | Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall Press, Upper Saddle River, NJ, USA |
| 2. | Toby Segaran. 2007. Programming Collective Intelligence (First ed.). O'Reilly |
| 3. | Tony J. Cleophas and Aeilko H. Zwinderman. 2015. Machine Learning in Medicine - a Complete Overview. Springer |
| 4. | Sunila Gollapudi, S. 2016. Practical Machine Learning. Packt Publishing Ltd |
| 5. | Peter Harrington. 2012. Machine Learning in Action. Manning Publications Co., Greenwich, CT, USA |
| 6. | Selected seminal and contemporary readings from peer-reviewed literature such as Proceedings of Machine Learning in Healthcare, Artificial Intelligence in Medicine, IEEE Transactions on Biomedical and Health Informatics |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 3 | 4 | 98 |
| Assignment | 7 | 0 | 5 | 35 |
| Individual Work | 7 | 3 | 3 | 42 |
| Midterm Examination | 1 | 9 | 2 | 11 |
| Final Examination | 1 | 12 | 2 | 14 |
| TOTAL WORKLOAD (hours) | 200 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | |
OÇ-1 | 3 | 4 | 4 | 4 | 4 | 3 | 4 | 3 | 3 | 4 | 3 | |
OÇ-2 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | ||
OÇ-3 | 4 | 5 | 5 | 5 | 4 | 4 | 4 | 3 | 4 | 4 | ||
OÇ-4 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 3 | 4 | 3 | ||
OÇ-5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 4 | |||