Phelma Formation 2022

5PMBMLD0 : Introduction to Machine learning and Deep learning - WPMBDAS9

  • Number of hours

    • Lectures 12.0
    • Projects 0
    • Tutorials 0
    • Internship 0
    • Laboratory works 12.0

    ECTS

    ECTS 3.0

Goal(s)

Provide a solid introduction to machine-learning fundamentals, then delve into state-of-the-art deep-learning techniques and their practical applications in medical imaging

Contact Dawood AL CHANTI

Content(s)

Introduction to basic and advanced deep-learning techniques for medical-image analysis

1. Machine-Learning Fundamentals: supervised classification & regression (linear and logistic)
2. Deep Feed-Forward Networks: fully connected (multilayer perceptron) architectures
3. Convolutional Neural Networks (CNNs): feature extraction for 2-D, 3-D and multi-modal images
4. Representation Learning with Autoencoders: latent-space modeling and anomaly detection
5. Explainable AI & Uncertainty Quantification: XAI methods tailored to clinical decision-making
6. Generative Models: GANs, VAEs and hybrid approaches for data augmentation and synthesis
7. Diffusion Models: state-of-the-art denoising and high-fidelity image generation
8. MedSAM: “Segment Anything” for Healthcare: prompt-based foundation models for universal medical-image segmentation



Prerequisites

Python language MANDATORY
1st year mathematics course

Test

Semester 9 - The exam is given in english only 

Continuous assessment
BE report + MCQ + Project



Rapport de BE : 50%
Examen Ecrit : 50%

Additional Information

Semester 9 - This course is given in english only EN

Course list
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->Master->Semester 9

Bibliography

Deep Learning, An MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville
https://www.deeplearningbook.org/