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2024 course edition

Second course edition, taught at UNAM in 2024.

Instructors

• instructor: Sébastien Valade (UNAM)
• invited instructor (lecture 10): Ronny Hänsch (TUB)

Schedule

Introduction

• Introduction (slides)

Week 1

• Lecture: Python (slides)
• Tutorial: Python (notebook, notebook slides)
• Tutorial: Jupyter (notebook, notebook slides)

Week 2

• Lecture: Images (slides)
• Tutorial: Numpy (notebook, notebook slides)
• Exercises: notebook

Week 3

• Lecture: Filtering (slides)
• Exercises: notebook

Week 4

• Lecture: Morphology & Segmentation (slides)
• Exercises: notebook

Week 5

• Lecture: Homography (slides)
• Exercises: notebook

Week 6

• Lecture: Features & Motion Estimation (slides part 1, slides part 2)
• Exercises: notebook

Week 7

• Lecture: Machine Learning I - regression (slides)
• Exercises: notebook

Week 8

• Lecture: Machine Learning II - classification part-1 (slides)
• Exercises: notebook

Week 9

• Lecture: Machine Learning III - classification part-2 (slides)
• Exercises: notebook

Week 10

• Lecture: Machine Learning IV - Random Forests (slides, by Ronny Hänsch)

Week 11

• Lecture: Deep Learning I - shallow neural networks (slides)
• Exercises: notebook

Week 12

• Lecture: Deep Learning II - deep neural networks (slides)
• Exercises: notebook

Week 13

• Lecture: Deep Learning III - custom project & dataset (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