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Syllabus

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aronwc edited this page Aug 15, 2013 · 38 revisions

Table of Contents

Overview

  • Course: CS595: Machine Learning and Social Media
  • Instructor: Dr. Aron Culotta
  • Meetings: 1:50-3:05 p.m. Stuart 204
  • E-mail: aculotta at iit.edu
  • Office Hours: T/R 11:00 a.m. - 12:00 p.m.
Description: This seminar will explore the latest research developing machine learning methods to analyze online social media. Topics include: sentiment classification, information extraction, clustering, topic modeling, information diffusion, and social network analysis. Emphasis will be placed on the application of this technology to areas such as public health, crisis response, politics, and marketing.

Grading

  • 100 points - Paper summaries (10 @ 10 points each)
  • 100 points   - Paper presentations (2 @ 50 points each)
  • 200 points - Project (1 @ 200 points each)
  • 400 total points

Assignments

Paper Summaries

You will write 10 paper summaries (one per week). On the first day of class, the instructor will assign which papers you will write summaries for. Summaries are due the night before the paper is discussed.

  1. Read the article listed in the paper directory. schulz13multi
  2. Create a new file in the directory with your iit email name (e.g., aculotta.md)
  3. Add your summary and click "Commit Changes"
Each summary should contain the following:
  • Overview: Write a short paragraph summarizing the content of the paper.
  • Algorithm: Describe in more detail the primary algorithm proposed or applied in the paper.
  • Hypothesis: List the hypotheses the authors test in the paper (note that these are not always explicitly stated).
  • Data: Describe the data used in the experiments
  • Experiments: Briefly describe how are the experiments are organized.
  • Results: Describe the results and their significance.
  • Assumptions: List some of the important assumptions the authors make in their work.
  • Questions: List 2-3 questions you have about the paper.
  • Related Papers: List 2-3 papers that are most similar to this paper. For each, briefly list how this paper is different.

Paper Presentation

For a subset of the papers that you write summaries for, you will also present your summary to the class and lead the discussion. The presentation should be a more detailed version of the summary. In addition to the components above, the presentation should contain discussion questions for the class.

Project

The 200 points is broken down into:

  • 50 points - Presentation: Is the presentation clear, well-organized, and thorough?
  • 50 points - Code: Can I reproduce your results by running your code? Is the code well-written, debugged, and documented?
  • 50 points - Report: Follow the similar format as the papers we read for class. Your report should be 4-6 pages, including all references and figures. Are the main algorithms, hypotheses, and assumptions clearly stated? Are the comparisons with related work sound?
  • 50 points - Scientific rigor: Are your claims supported by the experimental results? Have you attempted to rule out all other reasonable competing hypotheses? Are the experiments soundly developed and executed?

Tips

Reading a research paper

Writing a response paper

  • What is the central hypothesis?
  • What are the assumptions made?
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