2021 course edition
First course edition, taught at UNAM in 2021.
You can open all notebooks in binder 
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Instructors
- instructor: Sébastien Valade (UNAM)
- co-instructor: Manuel Wöllhaf (TUB): lectures 6 (part 1), 7, 10, 11, 12
Lectures
Introduction
- Introduction: slides
Lecture 1: Python & Jupyter crash course
Lecture 2 (CV1): Digital image basics
Lecture 3 (CV2): Filtering
Lecture 4 (CV3): Morphology & Segmentation
Lecture 5 (CV4): Homography
Lecture 6 (CV5): Features & Motion Estimation
- Lecture: slides part 1, slides part 2
- Exercises: notebook
Lecture 7 (ML1): Machine Learning I
Lecture 8 (ML2): Machine Learning II
Lecture 9 (ML3): Machine Learning III
Lecture 10 (DL1): Deep Learning I
Lecture 11 (DL2): Deep Learning II
Lecture 12 (DL3): Deep Learning III
Recommended Literature
- Szeliski, R. (2021). Computer Vision: Algorithms and Applications, second edition
- James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning : with Applications in R. New York, Springer
- Chollet, F. (2017). Deep learning with Python. Manning Publications.
- Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (2nd ed.). O’Reilly.