Fall2020/index.html at master · neuroimagingdatascience/Fall2020 · GitHub

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<title>NEUR-608-Fall2020</title>
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  <h1 id="about">NEUROIMAGING DATA SCIENCE (NEUR-608)</h1>
  <p>The goal of our class is to familiarize students with several powerful analytical approaches that can be applied to complex datasets, such as those derived from modern neuroimaging.  After providing the basics of neuroimaging and statistical analysis, we will cover unsupervised as well as supervised learning, associative techniques and causal models, and give an introduction into graph theoretical analysis and meta-analyses. We will also provide guidelines for effective data visualization. A basic understanding of statistical analysis and MATLAB/python programming are prerequisites to this course.
	  Learning Objectives. </br>
</br>By the end of the course, the students should be able to: </br>
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1. Have an understanding of the covered analytical techniques. </br>
2. Be able to implement these techniques in their own data. </br>
3. Propose a neuroimaging analysis project in which these techniques are applied.</br>
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Teaching method. In the 3-hour long seminar, the instructors will first provide a brief overview of the methodology.  Students will read the assigned articles prior to the class and prepare a critical summary of the article’s strengths and weaknesses. One student will present the article to the class, and lead the discussion. In the second part of the class, students will carry out practical exercises on some of the covered techniques on their own laptop, with guidance by both instructors. As a final assignment, students will present a mock research paper with analyses utilizing one or more of the covered methods.
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<p>The syllabus, presentations, codebase etc can be found on the github <a href="https://github.com/neuroimagingdatascience/Fall2020/" target="_blank">here</a> </p>
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<p>If you have further questions, please get in touch with us via email:</br></br>
boris dot bernhardt at mcgill dot ca | bratislav dot misic at mcgill dot ca
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