**Introduction Outline** [Back to Neural Networks](https://cs.pomona.edu/classes/cs152/) # Project Milestones 01. [Individual Proposals (due week 3)](01-proposals.mdeep.html) 02. **Introduction Outline (due week 4)** 03. [Related Works Search (due week 5)](03-related-works-search.mdeep.html) 04. [Project Update 1 (due week 6)](04-update1.mdeep.html) 05. [Self-Assessment and Peer Evaluations 1 (due week 6)](05-evaluations1.mdeep.html) 06. [Introduction and Related Works Draft (due week 7)](06-introduction-related-works-draft.mdeep.html) 07. [Project Update 2 (due week 8)](07-update2.mdeep.html) 08. [Methods Outline (due week 9)](08-methods-outline.mdeep.html) 09. [Self-Assessment and Peer Evaluations 2 (week 10)](09-evaluations2.mdeep.html) 10. [Discussion Outline (due week 11)](10-discussion-outline.mdeep.html) 11. [Complete Rough Draft (due week 14)](11-completed-rough-draft.mdeep.html) 12. [Final Self-Assessment and Peer Evaluations (due Monday finals week)](12-evaluations3.mdeep.html) 13. [Complete Project and Revisions (due Wednesday finals week)](13-completed-project.mdeep.html) # Requirements The next milestone is an outline of your introduction. You will create and submit this the same way as your proposals (as a link to a simple web-page on gradescope) except that you will work on this in groups. **This project URL will be used and updated throughout the semester. So make sure that you like it.** The outline must include this information: - An (optionally revised) project name and project scope - A list of two to four group members - A five to ten sentence outline + You may follow Dr. Stirewalt’s guidelines for writing an introduction (see below), but it is not mandatory. + You should not "fill-in" each paragraph; you should **only provide the first sentence of each paragraph.** + You should explain your *ideal/expected* results for the "details" and "assessment" paragraphs. - A the ethical sweep (see below) You should think of this as an easy way to "build" your final write-up. Starting with the first sentence of each paragraph lets you focus on high-level details instead of the details. # Advice - Your group should come to an agreement about your project. It does not need to resemble the proposal. - Likewise, you can certainly expect your outline to evolve as you work on your project throughout the semester. > The Writing Center is open this semester! We open at full capacity after the second week of the semester, but we will be holding limited hours as soon as classes begin. Writing and Speaking Partners meet one-on-one with students to talk about their work and provide feedback at any stage of their preparation process. Trained to think deeply about rhetoric and communication across the curriculum, these student peers facilitate conversations about everything from ID1 papers to senior theses, lab reports to creative writing, giving presentations to developing strategies for reading and engaging more deeply in class discussions. Additionally, Jenny Thomas, our Assistant Director of College Writing and Language Diversity, offers specialized writing and speaking support for multilingual students navigating English as an additional language. To make an appointment with a Writing or Speaking Partner, please log on to the Portal and go to Academics > Writing Center, or contact us at writing.center@pomona.edu. All appointments will be made through the Portal as usual, will be online--or sometimes in-person but outdoors--and our Writing and Speaking Partners will be flexible both about the mode of consultation (phone, Zoom, email, Google docs, walk and talk, etc.) and about their hours in order to accommodate student need. # Stirewalt's 5-paragraph rule for writing Introductions > "Of the many tasks involved in writing a good conference paper, I find writing the introduction section to be the most difficult. This is unfortunate, as a poorly structured argument sets the wrong tone for what might otherwise be really good research.

> To help manage this painful process, I have developed a heuristic, called the **five-paragraph rule**, that is useful for organizing introductions. The heuristic prescribes that good introductions should contain a sequence of five major pieces, each of which should fit into a single paragraph in order to force the writer to communicate at the appropriate level of abstraction.

> The heuristic borrows ideas from **persuasive argument** and **structured analysis/structured design** (ala DeMarco/Yourdon), and it is reminiscent of a similar structuring mechanism from freshman level courses in English composition. My success in publishing papers increased dramatically once I began to use this heuristic to structure my introductions." > > -- Dr. K. Stirewalk The heuristic is: Design your introductions to comprise five paragraphs whose purpose and contents are as follows: 1. **Introductory paragraph**: What is the problem and why is it relevant to the audience attending *THIS CONFERENCE*? Moreover, why is the problem hard, and what is your solution? You must be brief here. This forces you to boil down your contribution to its bare essence and communicate it directly. 2. **Background paragraph**: Elaborate on why the problem is hard, critically examining prior work, trying to tease out one or two central shortcomings that your solution overcomes. 3. **Transition paragraph**: What keen insight did you apply to overcome the shortcomings of other approaches? Structure this paragraph like a syllogism: Whereas $P$ and $P => Q$, infer $Q$. 4. **Details paragraph**: What technical challenges did you have to overcome and what kinds of validation did you perform? 5. **Assessment paragraph**: Assess your results and briefly state the broadly interesting conclusions that these results support. This may only take a couple of sentences. I usually then follow these sentences by an optional overview of the structure of the paper with interleaved section callouts. # Ethical Sweep I am not expecting you to be an expert in your problem domain. You do not need to do any research to answer these questions (as you would if I were paying you to complete a job), but I want you to provide thoughtful answers. **General Questions:** - Should we even be doing this? - What might be the accuracy of a simple non-ML alternative? - What processes will we use to handle appeals/mistakes? - How diverse is our team? **Data Questions:** - Is our data valid for its intended use? - What bias could be in our data? (All data contains bias.) - How could we minimize bias in our data and model? - How should we "audit" our code and data? **Impact Questions:** - Do we expect different errors rates for different sub-groups in the data? - What are likely misinterpretations of the results and what can be done to prevent those misinterpretations? - How might we impinge individuals' privacy and/or anonymity?