Merge branch 'master' of https://github.com/NeuroDataDesign/academicpages.github.io

Author: bdpedigo

_pages/calendar.md

@@ -5,15 +5,15 @@ author_profile: true
 ---
 
 
-## Semester 1 
+## Semester 1
 
 ### Sprint 0
 
 #### Reading assignments for Sprint 0
 
 1. Read [Code of Conduct](https://neurodata.io/about/agreements/#respect).
 2. Read [How to choose a project](https://bitsandbrains.io/2018/08/31/sig-and-feas.html).
-3. Read about [smart goals](https://www.mindtools.com/pages/article/smart-goals.html).
+3. Read about [smart goals](https://www.mindtools.com/pages/article/smart-goals.htm).
 4. Read [Figure checklist](https://bitsandbrains.io/2018/09/08/figures.html) and make a perfect figure in your slide.
 5. Read [Constituents of Decisions](https://bitsandbrains.io/2018/09/22/deciding-stuff.html). and [3 additional consituents for decisions under uncertainty](https://bitsandbrains.io/2018/09/23/probabilistic-decisions.html).
 6. Read [10 Simple Rules for Designing Learning Machines](https://bitsandbrains.io/2018/09/24/modeling-desiderata.html).
@@ -75,14 +75,6 @@ The deliverable for each team is to append to their google document the followin
 
 - An 8-page conference report with publication quality figures and 10+ references.
 - A notebook to reproduce each of the quantitative results from the report.
-- A 20-minute presentation presenting the motivation, challenge, action, and result of your work. 
+- A 20-minute presentation presenting the motivation, challenge, action, and result of your work.
 - A live demo during your presentation.
 - A link to your data derivatives deposited in an open access repository.
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_pages/syllabus.md

@@ -37,7 +37,11 @@ The duration and major goals of each sprint is detailed below:
 Teams will be formed prior to joining the course. Students will rank projects in a public survey (meaning all students will be able to see the project preferences of their peers). Based on these results, students will **self-organize** into teams. Once a team has been finalized, I will approve all members to enroll in NDD.
 
 ### Weekly Structure
-We have class three times per week. The bulk of the classroom time, however, is spent with student presentations. Each week each student presents their progress from the previous week, and their plan for the next week. Feedback is provided in written form to each student for each presentation, and verbal feedback is provided whenever it seems generally useful for other students. Each team also meets at least once more per week to prepare their presentation, and work together. About once per week we provide a lecture on some relevant data science content, typically content not available otherwise online.   
+We have class three times per week. The bulk of the classroom time, however, is spent with student presentations. Each week each student presents their progress from the previous week, and their plan for the next week. Feedback is provided in written form to each student for each presentation, and verbal feedback is provided whenever it seems generally useful for other students. Each team also meets at least once more per week to prepare their presentation, and work together. About once per week we provide a lecture on some relevant data science content, typically content not available otherwise online.
+
+This year, for Fall 2020, the teams will present on the following days:
+- Mondays: brainlit, graspy
+- Wednesdays: ndforest teams
 
 ### Motivation
 This course is based on my experience as an academic, and entrepreneur, an advisor, and an instructor. It is designed to be the best class you'll ever take. You will learn (by doing and getting feedback) the skills that I have found particularly useful in my endeavors. It will be organized into weekly sprints. Weekly progress will be reported documenting goals towards your team sprints, with sprint demo's to happen at the close of each sprint. Each team will be graded jointly on the basis of meeting the sprint goals, as well as providing clear and concise weekly progress reports.