Instructor: Yizhou Sun
Lecture times: T/R 10am-11:50am
Lecture location: BOELTER 5419
The course aims to introduce probabilistic graphical models for structured data, where data points are no longer independent with each other, such as sequential data and graph/network data. The course will cover modeling, inference, and learning of several classical probabilistic graphical models, including Hidden Markov Model, Markov Random Field, Conditional Random Field, and Factor Graph. Applications across different domains, such as text mining, medical domain, and social network analysis, will be discussed. The students are expected to read and present cutting edge research papers, as well as conduct a research oriented course project.
Part I: Lectures (5 weeks)
1. Introduction [slides] (0.5 lecture, Jan. 8)
2. Basic probabilistic models: Naive Bayes and Logistic Regression [slides] (1.5 lectures, Jan. 8, Jan. 10)
3. Hidden Markov Models [slides] (2 lectures, Jan. 15, Jan. 17)
4. Markov Random Fields [slides] (3 lectures, Jan. 22, Jan. 24, Jan. 29)
5. Conditional Random Fields [slides] (2 lectures, Jan. 31, Feb. 5)
6. Factor Graph [slides] (1 lecture, Feb. 7)
Part II: Paper Presentations (4-5 weeks, Feb. 12 - Mar. 14)
Part III: Course Project Presentation (Mar. 20, 8-11am)
*Note: all the deadlines are 11:59PM (midnight) of the due dates.
Q & A
This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.
Tips: Answering other students' questions will increase your participation score.
Find our class page at: https://piazza.com/class/jqmvc1y0frm3cp
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