Weekly Plan

25th February - Introduction and Workflows

Downloads	Videos
	Part 1 , Part 2 , Exercise

Exercises

• Getting started with python for image processing,

Supporting material for the exercises

• Introduction to Python and Jupyter for Data-Science

4th March - Image Enhancement

Downloads	Videos
	Part 1 , Part 2

• Slides on non-local means (static) Lecture Handout

Exercises

• Overview part 1-3
• Overview part 4
• 

11th March - Ground Truth: Building and Augmenting Datasets

Downloads	Videos
	Part 1 , Part 2

Exercises

• Data augmentation exercise

18th March - Basic Segmentation, Discrete Binary Structures

Downloads	Videos
	Part 1 Part 2

Exercises

• Overview

25th March - Advanced Segmentation

Downloads	Videos
	Part 1 Part 2

Exercises

• Overview

1st April - Analyzing Single Objects, Shape and Texture

Downloads	Videos
	Part 1 Part 2

Exercises

• Overview

8th April - Easter break

15th April - Analyzing Complex Objects

Downloads	Videos
	Part 1 Part 2

Exercises

• 
• Further exercises

22nd April - Statistics, Prediction, and Reproducibility

Downloads	Videos
	Part 1 Part 2

Exercises

• C. Elegans Dataset on Kaggle R Notebook or Python Notebook

• Will come later in python once we know more about Pandas: KNIME Exercises

29th April - Dynamic Experiments

Downloads	Videos
	Part 1 Part 2

Exercises

• Overview

6th May - Imaging with multiple modalities

Downloads	Videos
	Part 1 Part 2

Exercises

• Exercises on statistics and data frames

13th May - Ascension (no lecture)

20th May - Scaling Up / Big Data

Downloads	Videos
	Part 1 Part 2

Exercises

• Kaggle DAG Notebook for Filtering with Tensorflow, this a walk-through exercise. You can play with the number of iterations in the last part to see if there is data transfer bottle neck for GPU compared to CPU.
• Block-based 3D Image Analysis in Dask

3rd June - Project Presentations

Info before the presentations

Presentations

| Presenter(s) | Title. | Recording OK | | ————- | ————- |————–| |N.N.| TBA| yes|