2026 course edition
Third course edition, taught at UNAM in 2026.
Note: This edition puts more emphasis on Deep Learning, including group projects, at the expense of some Computer Vision and Machine Learning content covered in prior editions. The following topics, covered in 2024, were not taught this year: CV3 Morphology & Segmentation (slides), CV4 Homography (slides), CV5 Features & Motion Estimation (slides part 1, slides part 2), ML3 Principal Component Analysis (slides), ML4 Random Forests (slides).
Instructors
- instructor: Sébastien Valade (UNAM)
- invited instructor (projects): Sophie Giffard-Roisin (IRD - ISTerre)
Lectures
Introduction
- Introduction: slides
Lecture 1: Python & Jupyter crash course
- Lecture: slides
- Tutorial: Python (notebook, notebook slides)
- Tutorial: Jupyter (notebook, notebook slides)
Lecture 2 (CV1): Digital image basics
Lecture 3 (CV2): Filtering
Lecture 4 (ML1): Regression
Lecture 5 (ML2): Classification
Lecture 6 (DL1): Shallow Neural Networks (MLPs)
Lecture 7 (DL2): Convolutional Neural Networks (CNNs)
- Lecture: slides, classification roadmap
- Exercises: notebook
Lecture 8 (DL3): Convolutional Encoder-Decoder achitectures
- Lecture: slides
Projects
Group project (Weeks 11-14), in collaboration with Sophie Giffard-Roisin (IRD - ISTerre)
- Projects: slides
Recommended Literature
- Alegre, E., Pajares, G., de la Escalera, A. (2016). Conceptos y Métodos en Visión por Computador. CEA
- Szeliski, R. (2021). Computer Vision: Algorithms and Applications (2nd ed.). Springer
- Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras and TensorFlow. O’Reilly Media, Inc.
- James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer
- Chollet, F. (2021). Deep learning with Python. Manning
- Elgendy, M. (2020). Deep learning for vision systems. Manning