Curriculum Materials
Here is a general outline of the topics covered by each year-long course. The outlines for Volumes 1 and 2 are followed fairly closely, since the textbooks for those courses are complete. The outlines for Volumes 3 and 4 may vary from year to year as we refine the courses and update material to reflect the latest developments in the field.
The textbooks for ACME are being published by the Society for Industrial and Applied Mathematics (SIAM). http://bookstore.siam.org/ot152/
The lab manuals and other materials are freely available at https://foundations-of-applied-mathematics.github.io
These are the instructions for getting your computer set up:
The fully compiled PDFs for the programming labs of volumes 1-4 are at the links below:
Junior Labs
For BYU students, the data files needed to complete the labs are found below:
For Prospective Adopters, the data files are found at the link below:
* Python Essentials
** Data Science Essentials
*** Labs are due at 9:00 am 1 week after they are assigned unless the next week is a slack week, then it is 2 weeks after. For example, UNIX Shell 1 is due on September 6th, but Linear Transformations is due October 11th.
First Semester
| Week | Volume 1 Lab (Thursday) | Volume 2 Lab (Tuesday) |
| 1 | **UNIX Shell 1 | LABOR DAY |
| 2 | *Standard Library | Intro to Python |
| 3 | *OOP | NumPy |
| 4 | *Exceptions/IO | MatPlotLib |
| 5 | Linear Transformations | *Unit Testing |
| 6 | Linear Systems | SLACK DAY |
| 7 | SLACK DAY | BSTs |
| 8 | The QR Decomposition | Nearest Neighbors |
| 9 | Least Squares and Computing Eigenvalues | BreadthFirstSearch |
| 10 | Image Segmentation | Dijkstra (New) |
| 11 | The SVD and Image Compression | Markov chains |
| 12 | Facial Recognition using Eigenfaces | Sampling Lab |
| 13 | THANKSGIVING | DFT |
| 14 | **SQL 1 | Convolution |
| 15 | NONE | NONE |
Second Semester
| Week | Volume 1 Lab (Tuesday) | Volume 2 Lab (Thursday) |
| 1 | No Class | Waveletes |
| 2 | **SQL 2 | Polynomial Interpolation |
| 3 | *Introduction to SymPy | Gaussian Quadrature |
| 4 | Differentiation | Line Search |
| 5 | Conditioning and Stability | SLACK DAY |
| 6 | Monte Carlo Integration | **Regular Expressions |
| 7 | Visualizing Complex-valued Functions | Gradient Descent Methods |
| 8 | President's Day | The Simplex Method |
| 9 | The PageRank Algorithm | Gymnasium |
| 10 | *Profiling | Convex opt |
| 11 | SLACK DAY | Non-negative Matrix Factorization |
| 12 | *Data Visualization | Interior Point 1: Linear Programs |
| 13 | **UNIX Shell 2 | Dynamic Programming |
| 14 | Iterative Solvers | Policy Function Iteration |
| 15 | REVIEW | REVIEW |
Senior Labs
For BYU Students, customized Volume 3 data files can be found here and Volume 4 data files can be found here.
First Semester
| Week | Volume 3 (Tuesday) | Volume 4 (Thursday) |
| 1 | Labor Day | Animation |
| 2 | Pandas 1: Intro | Intro to IVP and BVP Solvers |
| 3 | Pandas 2: Plotting | SIR |
| 4 | Pandas 3: Grouping and Pivot Tables | IVP |
| 5 | Info + Wordle | SLACK DAY |
| 6 | Pandas 4: Geopandas | Predator-Prey |
| 7 | SKLearn and LSI | Lorenz |
| 8 | Data Cleaning and FE | Bifurcation |
| 9 | RF and Decision Trees | Finite Difference |
| 10 | SLACK DAY | Heat Eqn |
| 11 | Kmeans | Wave Eqn |
| 12 | OLS | Anisotropic |
| 13 | Parallel | THANKSGIVING |
| 14 | Logistic Regression | Finite Element |
| 15 | SLACK DAY | SLACK DAY |
Second Semester
| Week | Volume 3 (Tuesday) | Volume 4 (Thursday |
| 1 | Naive Bayes | Poisson's Equation |
| 2 | Choose one( Apache Spark, Parallel Programming with MPI, Web Scraping or, Web Crawling) | Spectral 1: Method of Mean Weighted Residuals |
| 3 | Metropolis Algorithm | Spectral 2: A Pseudospectral Method for Periodic Functions |
| 4 | Gibbs Sampling and LDA | Inverse Problems |
| 5 | Gaussian Mixture Models | The Shooting Method for Boundary Value Problems |
| 6 | Discrete Hidden Markov Models | Total Variation and Image Processing |
| 7 | President's Day | Transit Time Crossing a River |
| 8 | Speech Recognition Using CDHMMs | HIV Treatment Using Optimal Control |
| 9 | Kalman Filter | Solitons |
| 10 | ARMA Models | SLACK DAY |
| 11 | Project Day | Obstacle Avoidance |
| 12 | Non-Negative Matrix Factorization Recommender | The Inverted Pendulum |
| 13 | Deep Learning | Timber Harvesting |
| 14 | Recurrent Neural Networks | Project Day |