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MACF 491

Topics in Mathematical and Computational Finance

3 credits · Undergraduate

Fall 2026 · Topics in Statistics

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 · Introduction to Neural Networks

Topics

  • Predictive analytics, supervised learning and cross-validation
  • Numerical overflow and underflow
  • Feed-forward networks and universality
  • Classification and regression architectures
  • Objective functions and parameter estimation
  • Gradient descent, backpropagation and Hessian computation
  • Initialization and adaptive learning rates
  • Regularization, augmentation and noise injection
  • Recurrent and long short-term memory networks
  • Convolutional networks
  • Python or R implementation and applications

Readings

  • Reference (not explicitly required): C. C. Aggarwal. Neural Networks and Deep Learning: A Textbook, 2nd edition. Springer, 2023.
    Listed reading
  • Reference (not explicitly required): C. M. Bishop and H. Bishop. Deep Learning: Foundations and Concepts. Springer, 2024. https://www.bishopbook.com/
    Listed reading
Winter 2026 · Statistical Learning

Topics

  • Cross-validation
  • Regression, generalized linear and generalized additive models
  • Variable selection
  • Ridge regression and LASSO
  • Classification and nearest neighbors
  • Linear and quadratic discriminants
  • Logistic regression and support vector machines
  • Tree-based methods
  • Introductory neural networks
  • Unsupervised learning
  • K-means and hierarchical clustering
  • Principal-component analysis

Readings

  • Listed textbook (requirement not stated): G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning. Springer. https://www.statlearning.com/
    Listed reading
  • Listed textbook (requirement not stated): J. Friedman, T. Hastie and R. Tibshirani. The Elements of Statistical Learning. Springer Series in Statistics. https://web.stanford.edu/~hastie/ElemStatLearn/
    Listed reading
Winter 2026 · MACF 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2026 · STAT 380

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2026 · STAT 385

Readings

  • Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.
    Listed reading; requirement unspecified
  • Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.
    Listed reading; requirement unspecified
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 · ACTU 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2025 · MACF 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2025 · MACF 491

Readings

  • Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.
    Listed reading; requirement unspecified
  • Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.
    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 380

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2025 · STAT 385

Readings

  • Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.
    Listed reading; requirement unspecified
  • Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.
    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 · ACTU 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2024 · MACF 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    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 380

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    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 2023 · ACTU 491

Readings

  • I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, Springer.
    Listed reading; requirement unspecified
  • B. Oksendal, Stochastic Differential Equations, Springer.
    Listed reading; requirement unspecified
Fall 2023 · MACF 491

Readings

  • I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, Springer.
    Listed reading; requirement unspecified
  • B. Oksendal, Stochastic Differential Equations, Springer.
    Listed reading; requirement unspecified
Winter 2023 · MACF 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2023 · STAT 380

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2022 · ACTU 491

Readings

  • I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, Springer.
    Listed reading; requirement unspecified
  • B. Oksendal, Stochastic Differential Equations, Springer.
    Listed reading; requirement unspecified
Winter 2022 · MACF 491

Readings

  • I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, Springer.
    Listed reading; requirement unspecified
  • B. Oksendal, Stochastic Differential Equations, Springer.
    Listed reading; requirement unspecified
Winter 2022 · MACF 491

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    Listed reading; requirement unspecified
Winter 2022 · STAT 380

Readings

  • An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.
    Listed reading; requirement unspecified
  • The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.
    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