University of Minnesota
CSci 8363 - Linear Algebra in Data Exploration
CSci 8363 -- Fall 2022 -- Semester Schedule

CSci 8363 -- Fall 2022 -- Semester Schedule

Schedule for the semester

General Information

  • CSci 8363 - Mon Wed 4-5:15 in Amund 116. (no UNITE).
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  • Some outside references may work only from "" hosts. Authentication or a VPN connection might be necessary to access them from off-campus.


  • Students are to present [at least] one or two research or tutorial papers during the course of the semester, by rotation. Talks should highlight the main points, and summarize the theoretical and experimental results present in the paper being presented. For very theoretically technical papers, you should at least be able to explain what the main results are and what they mean, even if the detailed derivations are too complex for a short presentation. Long papers may be split up into parts presented separately (e.g., basic results/algorithms and examples/applications).
  • The papers listed here are a mix of historical foundational papers, recent papers showing variations on the original ideas or how the abstract methods have been applied to specific applications. Students should either select from among these papers, or may select another similar relevant paper after checking with first with the instructor.
  • Submit a weekly synopsis of each week's material, with your own reactions. This should be limited to a paragraph or two pointing out what the main take-away message you got out of each lecture.
  • Also submit as separate item some comments on how the lecture was presented, to be passed on to the speaker (without attribution).
  • Develop and carry out a research project based on one or more recent research papers devoted to topics studied in this class. A research project can be a literature survey, an experimental study of some methods proposed in a paper or of an application of one of the methods studied in this class. To give an approximate scale of the effort required, you should expect to devote about 50 hours of time during the course of the semester. In about 3 weeks, you should submit a 1 page description of your proposed project.
  • Write a 10-15 page report on your research project at a level that would be appropriate for publication in a workshop or conference.
  • Give a short presentation on your project during the last 2 weeks of the semester. Your project will count toward the Project Requirements for a Plan C MS degree in Computer Science.
  • All submissions should be via Canvas.
  • The final grade will be derived from
    • Presentation of Research Paper in class [c. 40%]
    • Synopses submitted weekly [c. 10%]
    • Project (including the proposal, the short presentation, and mainly the written report) [c. 50%]
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See this list of slots to fill below. You should select a slot from here and a paper from this list of papers. If you choose a survey or a review paper, or one before 2020, you should be prepared to present it in September or early October. You may also select a related paper not list here with instructor prior approval.

Week 1 - 9/07

Boley_____: Introduction

Week 2 - 9/12-9/14

Boley_____: Tutorial:Laplacian
Boley_____: Tutorial:Laplacian

Week 3 - 9/19-9/21

Boley_____: Graph properties in information retrieval
Boley_____: Graph Properties via Matrix Fcns

Week 4 - 9/26-9/28

Boley_____: Laplacian Eigenmaps
Boley_____: Neural Network Basics

Week 5 - 10/03-10/05

Silu______: Normalized cuts and image segmentation 
Camden____: Fast computation of commute times and katz scores (via Lanczos) date tentative 

Week 6 - 10/10-10/12

David H___: Gradient-Based Learnin / Document Recognition
Zach______: Semi-Supervised Classification with GCNs,
Project Proposal due 10/08/22

Week 7 - 10/17-10/19

Ivan______: Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer 
Mario_____: Loss landscapes and optimization ... 

Week 8 - 10/24-10/26

No Class__: Instructor out of town.
No Class__: Instructor out of town.

Week 9 - 10/31-11/02

Carl______: CNNs on Graphs with Fast Localized Spectral Filtering
Zach______: DeepWalk

Week 10 - 11/07-11/09

Boley_____: faster matrix multiplication via RL
Silu______: ML to denoise rapid acquisitoin spectroscopy

Week 11 - 11/14-11/16

Carl______: Hypergraph clustering (date tentiave)
Mario_____: Attention Is All You Need

Week 12 - 11/21

David_____: Deep Contextualized Word Representations
no class__: Paper TBA

Week 13 - 11/28-11/30

Ivan______: Volumetric graphs 
Camden____: Fast computation of digraph laplacian pseudo inverse

Week 14 - 12/05-12/07

no class__: No Paper
no class__: No Paper

Week 15 - 12/12-12/14

The order of talks on each day may change.

12/12: Camden____, Zach______, Carl______

12/14: David_____, Silu______, Mario_____, Ivan______

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