Merge pull request #998 from NeuromatchAcademy/staging

Staging

Author: iamzoltan

projects/README.md

@@ -19,18 +19,20 @@ Each pod will split into two groups. We recommend intentionally creating groups
 
 Note that reinforcement learning is an advanced topic at NMA, taught in week 3 just before the end. Also, NLP uses concepts from week 2, like recurrent neural networks and transformers. We also provide fewer project templates for these topics.
 
-For groups working on neuroscience projects, we have many curated datasets and materials available from the comp-neuro course ([here](https://compneuro.neuromatch.io/projects/docs/datasets_overview.html)). 
+For groups working on neuroscience projects, we have many curated datasets and materials available from the comp-neuro course ([here](https://compneuro.neuromatch.io/projects/docs/datasets_overview.html)).
 
-In the first week, you will lay the foundation for your projects by finding an interesting question to ask and an appropriate dataset for answering the question. Week 1 Day 4 (W1D4) is a **Projects Day**, when you will perform a literature review and refine your project idea and question. We encourage you to be creative with your question! 
+In the first week, you will lay the foundation for your projects by finding an interesting question to ask and an appropriate dataset for answering the question. Week 1 Day 5 (W1D5) is a **Project Wildcard Day**, when you will study a day in the curriculum that is related to your project. When the day of the curriculum you have studied comes around, it will then be your **Project day**. In addition to studying a day in the curriculum on your Project wildcard day, you should conduct preliminary literature review and review/refine your research questions. When your Project day comes around, you could then use this to continue working on your project, depending on how far into the project you have gone at the time.
 
-During the second week, you will refine your question iteratively as you try things out. You will experiment with different types of deep neural networks to find one that works for your project. 
+
+During the second week, you will refine your question iteratively as you try things out. You will experiment with different types of deep neural networks to find one that works for your project.
 
 Week 3 is all about execution. Half of W3D2 is dedicated to working on projects and writing an abstract. For the rest of the week, you will continue implementing your project and trying to get an answer to your question, culminating in a short presentation to your pod on the final day 15 (5 minutes, 3-5 slides, informal, take turns to present).
 
 # **Daily Schedule**
 
 Detailed schedule about what to do every day is in the [Daily student guide for projects](https://deeplearning.neuromatch.io/projects/docs/project_guidance.html). You should refer back to this document often.
 
+This year, project time will come after the tutorial/content time for all 5 time slots.
 
 # **Ten steps how to model materials**
 
@@ -42,8 +44,8 @@ Project templates ([here](https://deeplearning.neuromatch.io/projects/docs/proje
 
 # **Important deadlines**
 
-* W3D2:   by end of your project time, please submit your abstract via this [Airtable link](https://airtable.com/shrUeDqzGe8Cplk8u).  
-* W3D5:   by end of your project time, please submit your slides via this [Airtable link](https://airtable.com/shr5NJa397fSYNDsO).  
+* W3D2:   by end of your project time, please submit your abstract via this [Airtable link](https://airtable.com/shrUeDqzGe8Cplk8u).
+* W3D5:   by end of your project time, please submit your slides via this [Airtable link](https://airtable.com/shr5NJa397fSYNDsO).
 
 **Presentations** — Start preparing project presentation after W3D2
  * Groups make an approx five minute project presentation: 1 minute / 1 slide / 1 person
@@ -56,9 +58,9 @@ Project templates ([here](https://deeplearning.neuromatch.io/projects/docs/proje
   * Check the general schedule for precise timing of events on W3D5.
   * For each group, we will send an email invitation to the following people: TA, project TA and primary student contact.
   * More about scheduling and logistics in the [Student Guide to Projects](https://deeplearning.neuromatch.io/projects/docs/project_guidance.html)
-    
+
 # **Impact Scholars**
 If you would like to continue working on your project after the three-week course, we encourage you to apply to the [Impact Scholars program](https://impact-scholars.neuromatch.io/impact-scholars/structure.html).
 
 # **Project Examples**
-You can find examples from previous years' students [here](https://airtable.com/appoh6RKyBvxgiJ89/shrbgXZYZ2YIEiCY8). 
+You can find examples from previous years' students [here](https://airtable.com/appoh6RKyBvxgiJ89/shrbgXZYZ2YIEiCY8).

projects/docs/project_guidance.md

@@ -4,7 +4,7 @@
 
 This project plan explicitly encourages the iterative nature of research as a series of questions and answers that gradually refine your hypotheses. We have assigned you to pods based on your broad research interests (neuroscience, computer vision, natural language processing or reinforcement learning). Each pod will split into two groups alphabetically, with the goal of making well-balanced groups. If the split is not well-balanced (for example, all the coding experts in one group), then move one or two people around.
 
-Once you're in groups, you will start brainstorming and searching the literature for interesting papers, with the goal of forming a project question. For the rest of week 1 you will look for an appropriate dataset to answer your question, and try to process that dataset into a format that is good for modeling. Week 1 day 4 (W1D4) is entirely dedicated to projects.
+Once you're in groups, you will start brainstorming and searching the literature for interesting papers, with the goal of forming a project question. For the rest of week 1 you will look for an appropriate dataset to answer your question, and try to process that dataset into a format that is good for modeling. Week 1 day 5 (W1D5) is entirely dedicated to projects.
 
 In the second week you will experiment with different types of deep neural networks to try to answer your question. By W3D2, you will be able to write a short abstract about your project, which may or may not include results, but it should at least include a testable hypothesis. For the rest of the week, you will focus on getting evidence for/against your hypothesis.
 
@@ -26,11 +26,11 @@ Finally, on the last day of the course (W3D5), you will meet with other groups i
 
 Project TAs are your friendly topic experts to consult with on all issues related to your project. They can help with brainstorming, literature searches and coding. You will have meetings with them approximately every two days. During this time, they will help you refine your question and hypothesis into something that can be answered with deep learning. Sometimes they may miss a meeting or arrive late (busy schedules, lots of meetings!). In those cases, please stop what you were doing to have the meeting, and then resume your work when the project TA leaves.
 
-Sometimes the project TAs might need to schedule meetings slightly earlier or later. Please try to be flexible, they are doing a lot of "virtual footwork" to make sure all the groups have enough support during projects. We also encourage you to reach out to them for extra meetings whenever you need them, and to post questions on discord in the #topic channels. 
+Sometimes the project TAs might need to schedule meetings slightly earlier or later. Please try to be flexible, they are doing a lot of "virtual footwork" to make sure all the groups have enough support during projects. We also encourage you to reach out to them for extra meetings whenever you need them, and to post questions on discord in the #topic channels.
 
 ## Week 1: Getting started
 
-Depending on your time slot, you may or may not have project time on the first day of the course. Regardless of whether your first project meeting is day 1 or day 2, spend your first session doing the following:
+On the first day of the course (W1D1), spend your project time doing the following:
 
 * Split into groups. We recommend intentionally creating groups with diverse skill sets. Groups should have students who are very confident in Python and those who are just learning. Through the project, students can work together to strengthen each other's skills. We want to make sure that all members of each group get a chance to do all parts of the project. We ask that folks who are good with Python share what they know and hand off tasks to peers who are learning so they can improve their skills.
 * Introductions (30 min = 2 min/student): say a few things about yourself, then about your research area or research interests. What are you really curious about, that you might explore in your NMA project?
@@ -41,15 +41,29 @@ Depending on your time slot, you may or may not have project time on the first d
 Tips:
 * No need to have a very concrete project after this day. You will determine the feasibility of your questions in the next few days, and you will likely change your question completely. That’s how research works!
 
-In your next sessions, watch the [Modeling Steps 1-2 tutorials](https://deeplearning.neuromatch.io/projects/modelingsteps/ModelingSteps_1through2_DL.html). Continue brainstorming ideas. In practice, brainstorming and looking through related work are intertwined. You might want to get a headstart on looking at some literature, because it can inform and change your question.
+In your next sessions, watch the [Modeling Steps 1-2 tutorials](https://deeplearning.neuromatch.io/projects/modelingsteps/ModelingSteps_1through2_DL.html). Continue brainstorming ideas. In practice, brainstorming and looking through related works are intertwined. You might want to get a headstart on looking at some literature, because it can inform and change your question.
 
 * You will need to use your own project for some of this content. If you don’t yet have concrete ideas, or you haven’t done a research project before, use one of the provided project templates.
 * If you are using a project template, your goal is to translate the information from the slide and colab notebook into the 10-steps format. Some information might not be readily available in the slide or notebook, and you might have to find it in your literature review later this day.
 * Try to write down a few sentences for each of the two steps applied to your project. Putting thoughts into well-defined sentences and paragraphs helps at all stages of a project.
 
-## W1D4: Projects Day!
+## W1D5: Project Wildcard Day!
+
+This is a full day dedicated to projects! The goals are four-fold: study a tutorial related to your project, perform a literature search, refine your question, and try to find a good dataset.
+
+This means you’ll do a day of the curriculum from later in the course that will help you specifically with your project topic. When you reach the day of the curriculum you have already done, that will then become your dedicated Project Day. For example, if your pod is doing Reinforcement Learning for your project data set, on W1D5 you will complete tutorials scheduled for W3D4: Basic RL. Then, when W3D4 comes around later in the course, you will use that day as your dedicated Project Day, and you will not work on any formal tutorials.
+
+The Project wildcard has been created to help students gain fundamental understanding of the project specialisation/area before beginning the project. The idea is to help students in a pod study the tutorial content that is most related to their project area to give them relevant foundational understanding of the project area.
+
+The following table specifies the tutorial content that your pod is assigned based on your project dataset on the Project Wildcard Day (W1D5).
+
+|   Assigned Project Dataset  |   Tutorial Content for W1D5     |     New Project Day     |
+|-----------------------------|---------------------------------|-------------------------|
+| Computer Vision             | [W2D2: Convnets and DL Thinking](https://deeplearning.neuromatch.io/tutorials/W2D2_ConvnetsAndDlThinking/chapter_title.html)  | W2D2 (Tue., 15 July)    |
+| Natural Language Processing | [W3D1: Time Series and NLP](https://deeplearning.neuromatch.io/tutorials/W3D1_TimeSeriesAndNaturalLanguageProcessing/chapter_title.html)       | W3D1 (Mon., 21 July)    |
+| Reinforcement Learning      | [W3D4: Basic RL](https://deeplearning.neuromatch.io/tutorials/W3D4_BasicReinforcementLearning/student/W3D4_Tutorial1.html)                  | W3D4: (Thur., 24 July)  |
+| Neuroscience                | [W2D2: Convnets and DL Thinking](https://deeplearning.neuromatch.io/tutorials/W2D2_ConvnetsAndDlThinking/chapter_title.html)  | W2D2: (Tue., 15 July)   |
 
-This is a full day dedicated to projects! The goals are threefold: perform a literature search, refine your question, and try to find a good dataset.
 
 Start by watching the videos for [Modeling Steps 3-4](https://deeplearning.neuromatch.io/projects/modelingsteps/ModelingSteps_3through4_DL.html). It will help you define the ingredients for your project. In our case, this primarily involves finding a dataset. Finding a good dataset is a crucial step in all deep learning projects. It will make the rest of the project much easier, so you should spend a lot of time on this (both days). This goes hand in hand with formulating a hypothesis (step 4), because some datasets will naturally invite you to make a hypothesis, especially if you already have a well-defined question. Here's an example:
 
@@ -61,7 +75,7 @@ Notice that we didn't start with the dataset and that is important. It may seem
 To find the dataset, you can rely on searches, especially using the keywords you identified in your literature review. You can also go to a few very well organized websites/repositories that have links to datasets (see [these ones](https://deeplearning.neuromatch.io/projects/docs/datasets_and_models.html) for example). When you find a dataset, make sure you can load it easily into google colab, either by downloading it locally and then uploading to your google drive, OR (and this is preferable/easier, especially if you don't have good internet) by directly downloading the dataset into google colab. You should then start working on the reformatting of the data, to put it into a format that you can apply deep learning on it (usually a multi-dimensional numpy array, or a set of images with a dataloader object in Pytorch). It's good to organize your dataset into a nice format early on, because that will save you time later and will make it easier to think about how to apply models to your data.
 
 
-## W1D5 and W2D1 (3h/day): Toolkits and models
+## W2D1 (3h/day): Toolkits and models
 
 You should now have a question, a dataset and a hypothesis. One or more of these could be shaky/vague, and that is ok! As you start testing you hypothesis, the weak points of your project will become clear and then you can update what needs updating (question, dataset or hypothesis).
 
@@ -110,7 +124,7 @@ Abstract writing day should have helped you narrow down what results (positive o
 
 At the end of W3D4, you should also submit your slides via this [Airtable link](https://airtable.com/appoh6RKyBvxgiJ89/shr5NJa397fSYNDsO).
 
-## W3D5: Final Presentations 
+## W3D5: Final Presentations
 
 Please see final day schedule and shared calendars for timing details: [https://deeplearning.neuromatch.io/tutorials/Schedule/daily_schedules.html#w3d5-final-day-of-course](https://deeplearning.neuromatch.io/tutorials/Schedule/daily_schedules.html#w3d5-final-day-of-course)
 
@@ -120,23 +134,23 @@ This is the day where you present your project to other groups. Your project TAs
 
 * 10 minutes of meet & greet. Do a round of introductions (one TA calls out names from the zoom list). Everyone says their name, pod name, position, university and subject of study, as well as one interesting fact about themselves "Hi, I'm Jonny from the wiggly caterpillars and I am a PhD student at University of Notre Dame in Paris. I do neuroscience experiments in flies, and in my free time I like to go on long bike rides".
 
-* 30-40 minutes of presentations, including questions. Each group should speak for about 5 minutes (1 minute per person + 2 minutes of intro/discussion), and then take questions for 1-2 minutes. Try not to waste too much time on logistics: join the zoom link and go to the appropriate breakout room quickly. Then the student groups can start presenting in any order. 
+* 30-40 minutes of presentations, including questions. Each group should speak for about 5 minutes (1 minute per person + 2 minutes of intro/discussion), and then take questions for 1-2 minutes. Try not to waste too much time on logistics: join the zoom link and go to the appropriate breakout room quickly. Then the student groups can start presenting in any order.
 
 * 10-20 minutes of general discussion. Here are some ideas for questions you could ask:
   * What was missing in the dataset that you would have really liked to have?
   * Does anyone plan to continue working on this project in the future? Perhaps a few students from the multiple groups would like to continue together?
   * Which one of the 10 steps to modeling/research was hardest and why?
   * Based on your experience with the NMA project, what project would you most like to do next? Make up your own, or pick from the NMA projects (a different dataset or project template which you did not use).
   * What surprised you the most about the process of doing a project? In what way was this project most different from other projects you have done in the past.
-  * What technique did you learn at NMA which you think you can immediately apply to your own project (if you are currently doing research)?  
+  * What technique did you learn at NMA which you think you can immediately apply to your own project (if you are currently doing research)?
 
 
 ### Logistics:
 
-* The lead TA for your megapod will make multiple subgroups of 4-5 research teams from across the entire megapod. Each subgroup gets a separate breakout room for their session. If there are too many large pods in the megapod, there may be multiple zoom links in the megapod, so make sure you have the right one for your group. 
+* The lead TA for your megapod will make multiple subgroups of 4-5 research teams from across the entire megapod. Each subgroup gets a separate breakout room for their session. If there are too many large pods in the megapod, there may be multiple zoom links in the megapod, so make sure you have the right one for your group.
   * timeslots 1,3,5:   last hour of project time, -1:00 to 0:00 relative to start of your normal tutorial time (check the shared calendars in jupyterbook).
   * timeslots 2,4: after the course feedback session (check the shared calendars in jupyterbook).
-  There is a hard cutoff at the one hour mark, so the TAs must ensure everyone gets a turn to present.  
+  There is a hard cutoff at the one hour mark, so the TAs must ensure everyone gets a turn to present.
 
 * Use this presentation style ([google slides](https://docs.google.com/presentation/d/1A1uaYarVot9YyCdbAAB4VDvsQfK6emqq-TwIZ9xVNwo/edit?usp=sharing) or [powerpoint](https://osf.io/ky6fj/download)) or create your own style!