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STAT 497

Topics in Statistics

3 credits · Undergraduate

Fall 2026

Topics

  • Multi-armed bandit problems
  • Markov decision problems
  • Dynamic programming
  • Tabular Monte Carlo and temporal-difference methods
  • Policy gradients
  • Approximation methods
  • Deep reinforcement learning
  • Reinforcement-learning programming in R

Readings

  • Miguel Morales. Grokking Deep Reinforcement Learning. Manning.
    Recommended / optional / reference
  • Listed textbook (requirement not stated): R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction, 2nd edition. MIT Press. http://incompleteideas.net/book/the-book-2nd.html
    Listed reading
Fall 2026 · STAT 497

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
Winter 2026 · STAT 497

Readings

  • A. E. Kyprianou, Introductory Lectures on Fluctuations of Levy Processes with Application, 2nd edition, Springer.
    Listed reading; requirement unspecified
  • A. E. Kyprianou, Gerber-Shiu Risk Theory, Springer.
    Listed reading; requirement unspecified
Fall 2025

Topics

  • Introduction to sports analytics
  • Sports-analysis tools and development environments
  • Big data in sports
  • Types of sports data
  • Data-collection methods and technologies
  • Practical analysis of sports data
Fall 2025 · MACF 491

Readings

  • A First Course in Bayesian Statistical Methods, by Peter D. Hoff, Springer.
    Listed reading; requirement unspecified
  • Bayesian Data Analysis, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 3rd Edition, CRC Press.
    Listed reading; requirement unspecified
  • An Introduction to Bayesian Analysis: Theory and Methods, by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, Springer, 2009.
    Listed reading; requirement unspecified
Fall 2025 · STAT 497

Readings

  • A First Course in Bayesian Statistical Methods, by Peter D. Hoff, Springer.
    Listed reading; requirement unspecified
  • Bayesian Data Analysis, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 3rd Edition, CRC Press.
    Listed reading; requirement unspecified
  • An Introduction to Bayesian Analysis: Theory and Methods, by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, Springer, 2009.
    Listed reading; requirement unspecified
Winter 2025 · MACF 491

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
  • Grokking Deep Reinforcement Learning, by Miguel Morales, Manning Publications.
    Listed reading; requirement unspecified
Winter 2025 · STAT 497

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
  • Grokking Deep Reinforcement Learning, by Miguel Morales, Manning Publications.
    Listed reading; requirement unspecified
Winter 2024 · MACF 491

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
Winter 2024 · STAT 497

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
Fall 2021 · MACF 491L

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
Fall 2021 · STAT 497

Readings

  • Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.
    Listed reading; requirement unspecified
Sources