Information Package
baskent.edu.tr

Program of Biomedical Engineering (English 30%)

BME472

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Course Code
BME472
Semester
-
Theory+Practice
3 + 1
ECTS
5
Course TypeElective Course
Course LevelBachelor's Degree
Year of Study-
Semester-
ECTS5
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Explains the fundamental concepts of artificial intelligence and machine learning.
  2. Defines supervised, unsupervised, and reinforcement learning methods and applies them to examples.
  3. Applies data preprocessing and feature extraction methods for biomedical data.
  4. Recognizes deep learning architectures and applies them to biomedical image and signal data.
  5. Gains knowledge about clinical decision support systems and AI-based biomedical applications.
  6. Develops simple machine learning applications using Python, MATLAB, or similar software tools.
  7. Interprets, reports, and presents the results obtained.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme ComponentsBENG335 BENG226
Course DescriptionWithin the scope of this course, students will learn the fundamentals of artificial intelligence (AI) and machine learning (ML), acquiring knowledge and skills for applications in the field of biomedical engineering. The course covers the historical development of AI, fundamental algorithms, supervised and unsupervised learning methods, deep learning architectures, biomedical data analysis, image processing, signal processing, and their use in clinical decision support systems. Students will be able to apply machine learning algorithms to solve biomedical problems and will gain practical experience through project-based work.
Course Contents
WEEK TOPICS
1st Week Introduction to Artificial Intelligence: Definitions, History, and Importance in Biomedical Engineering
2nd Week Introduction to Machine Learning: Fundamental Concepts and Learning Paradigms
3rd Week Data Preprocessing, Feature Extraction, and Dimensionality Reduction Techniques
4th Week Supervised Learning Algorithms (Regression, Classification)
5th Week Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)
6th Week Decision Trees, Random Forests, and Support Vector Machines
7th Week Artificial Neural Networks and Introduction to Deep Learning
8th Week MIDTERM EXAM
9th Week Deep Learning Applications in Image Processing and Medical Image Analysis
10th Week Biomedical Signal Processing (EEG, ECG) and ML Applications
11th Week Clinical Decision Support Systems and AI in Healthcare
12th Week Reinforcement Learning and Its Applications in Robotic Systems
13th Week Current Research: AI-Based Diagnosis, Treatment, and Personalized Medicine
14th Week Project Presentations and General Course Review
Recommended or Required ReadingPattern Recognition and Machine Learning, Christopher Bishop, Springer.
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press.
Biomedical Signal and Image Processing with Artificial Intelligence, Academic kaynaklar.
Planned Learning Activities and Teaching Methods
  • Lecture
  • Presentation
  • Project
  • Problem Solving
  • Discussion
  • Questions/Answers
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Quiz 2 10
Project 1 30
Total(%) 70
Contribution of In-term Studies to Overall Grade(%) 70
Contribution of Final Examination to Overall Grade(%) 30
Total(%) 100
Language of InstructionEnglish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4 LO5 LO6 LO7
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11