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2021 course edition

First course edition, taught at UNAM in 2021.

You can open all notebooks in binder .

Instructors

• Sébastien Valade (UNAM)
• Manuel Wöllhaf (TUB): lectures 6 (part 1), 7, 10, 11, 12

Schedule

Introduction

• Introduction (slides)

Week 1

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

Week 2

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

Week 8

• Lecture: Machine Learning II (slides)
• Exercises: notebook

Week 9

• Lecture: Machine Learning III (slides)
• Exercises: notebook

Week 10

• Lecture: Deep Learning I (slides)
• Exercises: slides, notebook

Week 11

• Lecture: Deep Learning II (slides)
• Exercises: slides, notebook

Week 12

• Lecture: Deep Learning III (slides)
• Exercises: slides

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.