Announcement
Warm welcome from the workshop organizers!

We have some useful information for you:
1. Please fill in this short survey. It will take 1-2 minutes to complete and will help us enormously!
2. Download the data before the workshop
3. Workshop contents will be soon available here (in case you want to check the contents)
4. In case you want to reach us before the event, please write an e-mail directly to me (Kamil A. Kaczmarek: kkaczma6 [at-goes-here] mion.elka.pw.edu.pl).

We look forward to see you this weekend!

By popular demand, this workshop session is now proposed on both days. Each session will feature the same content.

We will show the participants how to apply standard machine learning methods, such as clustering, to a complex problem of brain structure segmenting. We will segment images of brain structures using several dimensionality reduction and clustering techniques. Although fairly straightforward, this is a very useful and standard approach to this kind of problems in neuroscience.

As a part of the workshop, we will walk the participants, step by step, through the process of data normalization, application of dimensionality reduction techniques and clustering for segmentation purposes. Participants will visualize intermediate results to better understand the problem. We will validate this approach with a couple of methods and explain the pros and cons of each one of them.

Our workshop will consist of exercises that focus on implementing missing parts of Python code. The code will be written in Python with Jupyter notebook used for visualization and execution purposes.

1. Participants will be able to segment medical images using basic machine learning and deep learning methods
2. Participants will be able to interpret dimensionality reduction and clustering outputs correctly, as well as understand the limitations of each method
3. Participants will have ready-to-use code that they may adapt to other problems of similar type

• Python programming skills (Laptop/PC with Python 2.7 installed)
• basic IT skills will be helpful (Git, Python virtualenv) but not required
• basic understanding of machine learning will be helpful but not necessary