Fall term = F
Winter term = W
Spring term = S
|
Course |
Term |
Title |
Cross-Listing |
|---|---|---|---|
| AMATH 231 | F, W, S | Calculus 4 | ~ |
| AMATH 242 | W, S | Intro to Computational Mathematics | CM 271, CS 371 |
| AMATH 250 | F, W, S | Introduction to Differential Equations | ~ |
| AMATH 251 | F | Introduction to Differential Equations (Advanced Level) | ~ |
| AMATH 271 | F | Introduction to Theoretical Mechanics | ~ |
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| AMATH 331 | F, W | Applied Real Analysis | PMATH 331 |
| AMATH 332 | W, S | Applied Complex Analysis | PMATH 332 |
| AMATH 342 | F, W | Computational Methods for DEs | CM 352 |
| AMATH 343 | F | Discrete Models in Applied Mathematics | |
| AMATH 345 | F | Data-Driven Mathematical Models | |
| AMATH 350 | F, W | Differential Equations for Business and Economics | ~ |
| AMATH 351 | F, S | Ordinary Differential Equations 2 | ~ |
| AMATH 353 | W, S | Partial Differential Equations 1 | ~ |
| AMATH 361 | W | Continuum Mechanics | ~ |
| AMATH 373 | W | Quantum Theory 1 | ~ |
| AMATH 382 | W (even) | Computational Modelling of Cellular Systems | BIOL 382 |
| AMATH 383 | W (odd) |
Introduction to Mathematical Biology |
~ |
| AMATH 390 | F (even) |
Mathematics and Music |
~ |
| AMATH 391 | F (odd) | Fourier to Wavelets | ~ |
|
|
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| AMATH 442 | F | Computational Methods for PDEs | CM 452 |
| AMATH 451 | W | Intro to Dynamical Systems | ~ |
| AMATH 453 | F (odd) | Partial Differential Equations 2 | ~ |
| AMATH 455 | W | Control Theory | ~ |
| AMATH 456 | F | Calculus of Variations | ~ |
| AMATH 463 | F | Fluid Mechanics | ~ |
| AMATH 473 | F | Quantum Theory 2 | PHYS 454 |
| AMATH 474 | W | Quantum Theory 3: Quantum Information and Foundations | PHYS 484 |
| AMATH 475 | W | Intro to General Relativity |
PHYS 476 |
| AMATH 477 | F (odd) | Introduction to Applied Stochastic Processes | ~ |
| AMATH 495 | ~ | Reading Course | ~ |
Current Topics Courses
AMATH 495/900
- Mathematics of Climate Change
- Instructor: Prof. Barbara Zemskova
- Tue-Thu, 11:30-12:50
- This course will provide an introduction to mathematical techniques, including analytical, computational, and machine learning methods, used to study climate change. It is open to both upper-year undergraduate and graduate students. Course materials will examine both historical evidence of climate change and future predictions related to climatological and societal impacts based on current models. The course will have a strong computational component, though there are no formal pre-requisites. Students will, instead, be provided with sample codes in Python and will be expected to make simple extensions to these codes and analyze the outputs within the context of in-class discussions and readings. The students will also be expected to demonstrate their understanding of the scientific and technical skills by applying them to a dataset of choice and presenting them in a final project.
- Tentative list of topics:
- Analysis of historical evidence of climate change (Fourier transform, wavelet transform, regression analysis)
- Building blocks of climate models (equations of motion in fluid dynamics, sensitivity to boundary conditions and initial conditions, parameterizations and assumptions in climate modeling)
- Implications of climate model projections (societal impacts, uncertainty modeling, Bayesian statistics)
- Questions regarding the course can be directed to the instructor Professor Barbara Zemskova (barbara.zemskova@uwaterloo.ca).
- Introduction to the Mathematics of Deep Learning
- Instructor: Professor Jun Liu
- Mon-Wed, 14:30-15:50
- This course aims to give a theoretical introduction to the mathematics of deep learning. It is open to both upper-year undergraduate and graduate students. It is particularly suited for students with a strong background in advanced calculus, linear algebra, and introductory probability or statistics who are interested in the theoretical aspects of deep learning. Self-contained notes will be provided whenever possible, along with supplementary reading materials as needed.
- Tentative topics include:
- Introduction to learning with neural networks
- Approximation theory: Density, approximation degree, lower and upper bounds, benefits of depth, the curse of dimensionality
- Optimization theory: Gradient descent, accelerated gradient descent, stochastic gradient descent, convergence analysis, avoidance of saddle points
- Generalization theory: Generalization bounds, VC-dimension, Rademacher complexity, PAC-Bayes bounds, rethinking the generalization of deep neural networks
- Questions regarding the course can be directed to the instructor Professor Jun Liu (j.liu@uwaterloo.ca).