Statistical Relational Learning Seminar (CSCI 599)
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Statistical Relational Learning Seminar (CSCI-599)

Fall 2009 - Cancelled

General Information

Location:
Where: WPH 203
When: Tue, 2:00-4:50pm
Instructor:
Sofus A. Macskassy
Office: SAL 216
Office Hours: By appointment
Send me an email and I will be in the office before class.
Phone: 310-414-9849 x247
E-mail: macskass@usc.edu
NOTE: E-mail is the best way to reach me.

News

  • Sep 9, 2009: Course cancelled due to low enrollment.
  • Aug 25, 2009: Page put up.

Course Description

Statistical relational learning (SRL) is revolutionizing the field of automated learning and discovery by moving beyond the conventional analysis of entities in isolation to analyze networks of interconnected entities. In relational domains such as bioinformatics, citation analysis, epidemiology, fraud detection, intelligence analysis, and web analytics, there is often limited information about any one entity in isolation; instead it is the connections among entities that are of crucial importance to pattern discovery. Conventional machine learning techniques have two primary assumptions that limit their application in relational domains. First, algorithms for propositional data assume that data instances are recorded in homogeneous structures (i.e., a fixed number of attributes for each entity) but relational data instances are usually more varied and complex (e.g., molecules have different numbers of atoms and bonds). Second, the algorithms assume that data instances are independent but relational data often violate this assumption---dependencies may occur either as a result of direct relations or through chaining multiple relations together. For example, scientific papers have dependencies through both citations (direct) and authors (indirect). This seminar will provide an introduction to recent research in statistical relational learning. The course will survey recent approaches that combine probabilistic and logical representations to model relational and network datasets, focusing on fundamental challenges in representation, learning, and inference. We will review conventional graphical models and inductive logic programming approaches as needed for background. Classes will consist of instructor presentations, student presentations, and group discussions. Students will be required to (1) read, discuss, and present research papers, and (2) complete a semester-long class project. Potential projects include: investigating the performance of SRL algorithms, analyzing data with SRL models, design and implementation of SRL model/algorithm extensions.

Textbook:

  1. Required: readings from the current research literature, which will be available on class schedule page.
  2. Optional: Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, MIT Press, 2007.

Course Handouts

Assignments

  • Response papers to weekly readings
    • Students are required to write a response to two of the papers that we read in each class. The response papers should be emailed to the instructor and class discussant by 12:00 noon the day before class (Mon).
      • Papers should be at least a half-page and include, at minimum:
        1. A summary of the main contribution of the work.
        2. Two or more points of critique, question or praise of the work.
  • Paper presentations
    • Students are expected to present at least 3 papers throughout the semester (10-15 minutes perpresentationt; one paper at one lecture).
  • Leading class discussion
    • Students will be expected to start discussions in two or more lectures. This will include summarizing paper responses as a top-10 list and have two or more questions prepared to get discussion started.
  • Class participation
    • Students are expected to attend lectures and participate in discussion.
  • Class project
    • A class project involving SRL in some manner.

Grade breakdown

  • Response papers to weekly readings: 20%
    • Response papers will be graded on a scale of 1 to 5.
      Lowest two grades will be dropped.
  • Paper presentations: 20%
  • Leading class discussion: 20%
  • Class participation: 10%
  • Class project 30%

Each student is responsible for his/her own work. The standard departmental rules for academic dishonesty apply to all assignments in this course. Collaboration on homeworks and programs should be limited only to answering questions that can be asked and answered without using any written medium (e.g., no pencils, instant messages, or email).