Courses
Programme
First semester (30 ECTS)
According to their background (Computer Science, health, ...), students will take course:
- Either Python for Data Science and AI (6 ECTS) or Medical Imaging (6 ECTS)
and then
- Image Processing (6 ECTS)
- Machine Learning and Deep Learning (6 ECTS)
- Cloud Computing and Cybersecurity (6 ECTS)
- Hybrid and Distributed AI (6 ECTS)
Second semester (30 ECTS)
- Internship/MSc Thesis from February to June
Details of courses
Health, Digital Imaging
6 ECTS
Medical imaging
For students coming outside Health sector
- Fundamental concepts: X-ray and g-ray physics applied in medicine, Ultrasound and Doppler effect, Spin physics and basic imaging concepts,
- Introduction to DICOM format
- Introduction to imaging reconstruction: projections, filtered back-projection, Fast Fourier Transform
- Magnetic Resonance Imaging: Advanced technical considerations, Segmented k-space, Echo-Planar Imaging, Parallel Imaging
- Advanced imaging techniques in medicine: Angiography, Flow Quantification, Diffusion and Perfusion
- Other "imaging" techniques: EEG, fNIRS
- ...
Computer Science, Machine Learning
6 ECTS
Python for Data Science and AI
For students coming from Health Sector
- Introduction to Python and Computer Programming
- Data Types, Variables, Basic Input-Output Operations, Basic Operators
- Boolean Values, Conditional Execution, Loops, Lists and List Processing, Logical and Bitwise Operations
- Functions, Tuples, Dictionaries, and Data Processing
- Modules, Packages, String and List Methods, and Exceptions
- The Object-Oriented Approach: Classes, Methods, Objects, and the Standard Objective Features; Exception Handling, and Working with Files Numpy, Pandas, ...
- ...
Digital Imaging, Image processing
6 ECTS
Image Processing
- Introduction to digital image processing,
- Signals in 2 and more dimensions,
- Image enhancement,
- Image segmentation,
- Image registration,
- ...
Artificial Intelligence, Machine learning
6 ECTS
Machine Learning and Deep Learning
- Bayes decision theory,
- Parametric and non-parametric classification,
- Feature selection and extraction,
- Margins and Kernel based algorithms,
- Ensemble classification and learning,
- Deep learning: CNNs, RNNs, GANs,
- ...
Computer Science, Cloud, Security
6 ECTS
Cloud Computing and Cybersecurity
- Cloud Computing:
- History: going beyond virtualization
- Types of cloud computing: private, hosted, public and hybrid
- Cloud service models: SaaS, PaaS and IaaS
- Cloud computing concepts: virtualization, service-oriented architectures (SOA) and Web services
- Cloud subscription types: from mainframe to cloud
- Usages of cloud computing: actors, services, deployment examples
- Cloud architectures: N-tier, SOA and multi-tenancy
- Application scaling: monoliths, microservices, serverless
- Implementation approaches: DevOps, CI/CD, containers (Docker, Kubernetes)
- Cybersecurity:
- Breaches, threats, vulnerabilities
- Security principles: authentication, authorisation, minimum privileges, non-repudiation
- Risks: state of the art of vulnerabilities and threats, black clouds
- Security breaches: anatomy, DoS attack analysis
- Security standards: ITIL, ISO 27000
- Securing accesses: authentication protocols (Kerberos, SAP, ICP), access control
- Securing data: integrity, confidentiality, availability
- Cryptography: approaches and algorithms (public key certificates, digital signature, time-stamping, anonymity)
- AI for Cybersecurity
- Python libraries for AI and cybersecurity: NumPy, Scikit-learn, Matplotlib, Seaborn, Pandas, Pefile, Volatility
- Detecting Email Cybersecurity Threats with AI
- Malware Threat Detection
- Network Anomaly Detection with AI: classify network attacks, detect botnet topology
Computer Science, Data Science, AI
6 ECTS
Hybrid and Distributed AI
- Introduction
- Knowledge Engineering
- MultiAgents Systems
- Data Science
- Expert Systems
- ...