Introduction to Machine learning and Deep learning - 5PMBMLD0

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

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

Python language MANDATORY
1st year mathematics course