RDFIA – Pattern Recognition for Image Analysis and Interpretation
Objective: This course introduces key concepts and methods for the automatic analysis and interpretation of visual content in images. We propose modern machine learning approaches to explore fundamental and advanced methods for computer vision. A particular emphasis is placed on deep learning architectures and their training, which play a central role in today’s computer vision. The course addresses a broad spectrum of vision tasks, including image classification, segmentation, vision-language models, and generative modeling. Recent advances such as convolutional neural networks at scale, Vision Transformers, self-supervised learning, and diffusion models are covered in detail. Issues of model robustness, explainability, transfer learning, and domain adaptation are also discussed.
Theoretical lectures are complemented by hands-on programming sessions in Python, where students implement and experiment with the models studied. All about practicals: Here
Course 1 (Sept. 24, 2025)
- Introduction to Computer Vision and ML basics slides1
- Visual (local) feature detection and description
Bag of Word Image representation
Linear classification (SVM) slides2_SVM
Course 2
Introduction to Neural Networks (NNs) slides2_NN
NN training and Statistical decision theory slides3_NN
Course 3
Datasets, benchmarks and evaluation slides3_DATA
Neural Nets for Image Classification slides4_LargeConvNet
Course 4
Large Neural Nets for Image Classification slides5_LargeConvNet
Complements for intialization of NNs and normalization: BatchNorm, LayerNorm (convnet or transformer), InstanceNorm
Course 5
Vision Transformers slides_vit1
Details of Vision Transformers architecture: Embedding matrix, positional encoding, attention module, FFN module, Norms, classToken, …
Course 6
Segmentation with CNN slides_segmenta
Transfer learning and Domain adaptation slides7_Transfer
Course 7
Vision-Language models slides10.pdf
Course 8
Explaining&Monitoring VLMs
Course 9
Self-Supervised Learning in Vision
Course 10
Generative models with GANs slides8.pdf
Course 11 (Jan. 07)
Control 2pm-3:45pm + practicals 4pm-6pm
Conditional GANs and Teaser Diffusion models slides9.pdf
Course 12 Diffusion models for Image Generation (Alasdair)
Course 13 Bayesian deep learning (Clement)
Course 14 Failure and ood detection (Clement)
Prerequisites: Basic knowledge of digital image representation, statistical data processing, and scientific computing in Python.
Further reading (available at SorbonneU library):
Book Pattern Recognition and Machine Learning, C. M. Bishop
Book Deep Learning, I. Goodfellow, Y. Bengio, A. Courville
Book Computer Vision: Algorithms and Applications, Richard Szeliski