For this work, you will work individually. The goal of this assignment is for you to read one paper on fundamental research (in your area/area of interest) that makes use of some form of scientific computing (details below). You should carefully read this paper, consider what the paper is about, the details of the computing/simulation/modeling used in the paper, how the paper is related to this class, criticisms of the paper's experiments and setup, how the paper can be improved, and present a careful analysis of the contributions and results of the paper. In effect, you are to pretend that the paper is your own (you are its authors) and relate it to the class for us all to understand. Note that your choice must actually include a key computational component. Note that, since everyone comes a wide variety of backgrounds and domains, you cannot assume the class has the full background to understand all details of your paper/study. The key is that you are able to teach the class each and every detail clearly (which means you might need to look materials elsewhere to aid you. Do not assume any knowledge from your classmates except what we have covered in lecture. For example, if you present a topic in Monte Carlo methodology or a psychology-specific theory, you will need to teach the class the relevant core concepts and how it fits in the context of scientific computing.

Paper selection

Your papers should come from reputable sources (conferences or journals; ideally, these come from prominent/well-respected/well-known journals in your field or those of the computational sciences) related to the topic at hand. Your topic should be related to the course content. You can use the library database to find papers - your field will likely have top-tier journals or venues. Another place to consider is the end of each chapter in the textbook, where they discuss related works. For those papers, you might also use Google Scholar or CiteSeerX to find forward citations (i.e. those papers that cite the mentioned paper) which will be newer and may be more interesting. The paper must be at least 6 pages long, and the paper must be working on a topic or problem using machine learning, particularly any one of the methods we discussed in class or an extension of one thereof.

Deliverable: A final write-up, presentation slides, and a presentation given to the class on the final exam slot day.

Writeup (Due Dec 15, 12noon)

The writeup should carefully explain the papers referring to the current state of the art as relevant from the papers you have read. You should make sure to summarize as well as describe the specific advances from the paper in your own words, and to contrast them to the basic topics we have learned in the lecture. You are required to use LaTeX for the writeup, using the conference document class provided here (use this, and here is a basic starter template file you can modify, or use the equivalent on Overleaf). I expect the write-up to be about 2-3 pages of text content in length in this format (if you use figures, this means you will have more than 3 pages, but while you may have figures, you must a sufficient amount of meaningful text). We will be looking for (at least) a section with meaningful insight/content w.r.t. to the following:

• An intuitive, high-level overview of the paper. Why is the paper important? What does it do that had not been before it (what is novel)?
• A clear, well-written summary of the specific advances made in the paper, present key related math and computational concepts (equations/algorithms/proofs/theorems/lemmas) with careful explanation of each variable/operation and the general point of the equation (do NOT just throw/copy math or concepts from the paper -- you must digest and re-present it to demonstrate that you deeply understand it).
• A presentation and discussion of the scientific questions (in Cognitive Science) it seeks to answer?
• A discussion/analysis of how this paper relates and is different from the topics we cover this semester
• Clear and intuitive description of any concepts in the paper that were NOT cover this semester but needed to undestand the paper (for example, if your paper is heavily psychological or sociological, you must explain any necessary concepts from scratch in both your talk and paper; do not assume your audience knows your field).
• A discussion/conclusion where you thoughtfully present what you learned, what additional steps were needed to understand the paper beyond the fundamentals covered in this class, and, crucially what was wrong with the paper and what you would suggest is needed for improvement (or, what should be done to extend the paper for future work -- what would be critical to examine next).

Deliverable: You will hand in the compiled (PDF) report to a MyCourses dropbox. The report will be graded on formatting, style (coherence, clarity, completeness), content (at minimum does it adhere to the above criterion/bullet points).
NOTE: label your work file via the following convention (replace "lastnameX" with a teammate's surname): lastname_firstname_cogs621_final_report.pdf

Presentation (Due Dec 12, Talks start at 1pm; Slides due 12:50pm on MyCourses)

You will also be required to present to the class on your topic, approximately 20-25 minutes. You may use Powerpoint-style slides or Beamer (for example), as you see best to present the particular topic. Also note that your classmates should be well aware of the basic material, so you should review any course content very briefly before proceeding to the advances discussed in the papers. There should also be slides/content that address the same rough points described above for the report.

Deliverable: You will hand in any slides that you use to a MyCourses dropbox. The presentation will be graded on content (i.e. appropriate level for your fellow students), materials as appropriate, and presentation style (coherence, clarity, ability to answer questions). NOTE: label your slides file via convention:
lastname_firstname_cogs621_final_talk.[file_suffix]

Grading

• Slides/Presentation Content: 10%
• Final report: 55%
• Presentation: 35%