Course Syllabus

CSci 8363 -- Fall 2025 -- Announcement

CSci 8363 -- Fall 2025 -- Announcement

Linear Algebra in Data Exploration

Tentative Plan for the semester.

General Information

  • CSci 8363 - Mon Wed 4-5:15 in Akerman 211. (no UNITE).
    Class Canvas Page
  • Some outside references may work only from "umn.edu" hosts. Authentication or a VPN connection might be necessary to access them from off-campus.

General 2025 PLAN

  • Students are to present one or more 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.
  • A typical research project could be to implement a method proposed in a research paper on some new set of data collected from a new source (e.g., Kaggle).
  • 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 3 weeks of the semester. Your project and presentation 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 (updated)
    • Presentation of Research Paper in class (twice?) [c. 25%]
    • Synopses submitted weekly [c. 30%]
    • Project (including the proposal, the short presentation, and mainly the written report) [c. 30%]
    • Attendance and discussion [c.15%]

Partial list of topics

  • Linear classifiers, logistic regression, SVMs
  • linear algebra in supervised learning neural networks
    • MLPs, CNNs,
    • transformers
    • low-rank adaptation
  • linear algebra in unsupervised learning neural networks
    • Common latent space embeddings: word2vec, CLIP
    • Sparsity regularization in optimization (e.g. LASSO)
    • Sparse auto encoders
  • Linear algebra in reinforcement learning: learning fast matrix-matrix multiply
  • Kernel methods in machine learning (KSD)
  • Generative models, diffusion models.