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### [Practical 1 – Fundamentals of Image Processing](https://gesis.mybinder.org/binder/v2/git/https%3A%2F%2Fgitlab.tudelft.nl%2Faj-lab%2Fteaching.git/b6842bd87ae787ae9843a49a5b4fa4523b94822a?filepath=binder%2Fhri_practical01%2Fip_basics_part1.ipynb)
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Navigate to the `binder` folder and then start the file __`hri_practial01`__ folder and start ip_basics_part1.ipynb by clicking on it or simply click [here]().
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This should open a Jupyter notebook containing your practical assignment.
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<br>
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### Practical 2.5 – 3D reconstruction by projection matching and Maximum Likelihood methods
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* How to reconstruct 2D class averages from randomly oriented particle images
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* How this would extend to 3D cases.
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### [Practical 2 – 2D/3D reconstruction](https://edu.nl/tqpf8)
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TODO, Jupyter Notebook currently work in progress
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This practical will introduce you to the principles of single-particle reconstruction methods based on projection matching and Maximum Likelihood methods. You will learn about the pitfalls in assigning 3D orientations to particle images from cryo-EM experiments, the dangers of how a reference model can bias the outcome of a reconstruction if the noise level is high and you will explore some of the advantages of using probabilistic rather than deterministic models for orientation assignment.
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### [Practical 3 – Introduction to electron tomography](https://edu.nl/7kakp)
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