University of Texas at Austin

CSE 392 Topics in Computer Science

Advanced topics in the theory and application of computer science. Recent topics include geometric modeling and visualization, and high-performance and parallel computing. Three lecture hours a week for one semester. May be repeated for credit when the topics vary.

Recent topics

  • Foundations of Predictive Machine Learning (Fall 2020)
    Foundational aspects of data sciences, machine (deep) learning and statistical inference analysis.
  • Geometric Foundations of Data Science (Fall 2025)
  • Geometric Methods in Data Science (Fall 2021)
  • Introduction to the Mathematical Theories and Computational Methods for Machine Learning (Fall 2020)
    Theoretical and computational aspects of Bayesian inversion framework, MCMC theory, randomization methods for inverse/inference problems, and the theory of machine learning. Focus on mathematical understanding of Bayesian inference, learning problems, and their computations.
  • Matrix and Tensor Algorithms for Data (Spring 2024)
    Study mathematical foundations of large-scale data processing, design algorithms and learn to (theoretically) analyze them. Explore randomized numerical linear algebra (sketching and sampling), and tensor methods for processing and analyzing large-scale databases, graphs, data streams, and large multidimensional data. Discuss linear algebra concepts of quantum computing.
  • Parallel Computing for Sci/Engr (Spring 2026)
    Learn fundamentals of parallel programming for scientific and engineering applications. Study general parallel computing principles, including asymptotics; current computer architectures and how parallelism manifests itself in them. Focus on building active command in MPI and OpenMP. Focus primarily on CPU-based execution, with a short section on GPU execution through offloaded OpenMP and heterogeneous models such as Kokkos.
  • Physical Simultn Comp Graphics (Spring 2026)
    Explore the key concepts and algorithms for simulating physical systems; starting from the ground up with particle systems and mass-spring networks, rigid and elastic bodies, collisions, cloth, and fluids.
  • Predictive Machine Learning (Fall 2025)
    Explore foundational mathematical, statistical and computational learning theory and application of data sciences. Learn modern machine learning approaches in optimized decision making and multi-player games, involving stochastic dynamical systems, and optimal control.
  • Scientific Computing in Machine & Deep Learning (Fall 2024)
    Selected topics in numerical methods for machine learning and deep learning including non-convex optimization methods and second-order methods, kernel methods, neural ODEs, Bayesian inference, and reduced order methods for scientific machine learning.