• Introduction to medical data – medical systems
• Biomedical signal processing 
• Biomedical image processing
• 3D medical  data representation
• Introduction artificial intelligence
• Supervised and unsupervised learning
• Practical applications on classification

This course is using project-based assessment. Students are asked to complete an individual project on the following (indicative) topics, and their grade is based on the level of completeness, algorithm performance and documentation of the solution (100%):

• EEGs classification process
• TN for Tumor classification
• fMRI brain activity

• Acquire expertise in employing Artificial Intelligence (AI) and Deep Learning (DL) algorithms for the processing and examination of medical data, showcasing the capacity to enhance patient care through precise diagnosis and prognosis.
• Develop the ability to process and interpret bio-signals like electroencephalogram (EEG), electromyogram (EMG), and electrocardiogram (ECG) using AI and DL techniques, demonstrating expertise in bio-signal analysis.
• Acquire proficiency in the examination of two and three-dimensional medical image data, encompassing x-rays, CT scans, and magnetic resonance imaging (MRI), through the utilization of AI and DL techniques, with a focus on precise interpretation of medical images.
• Comprehend and employ diverse AI and DL techniques for the creation of Diagnostic Support Systems, emphasizing their capability to aid healthcare professionals in achieving more precise diagnoses and treatment choices.
• Utilize AI and DL algorithms with actual visual data and bio-signals, showcasing hands-on expertise in implementing these technologies within healthcare environments.
• Demonstrate the capacity to enhance patient care by effectively applying AI and DL techniques to increase the precision of diagnosis and prognosis in the healthcare sector.