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Curriculum Materials

Course Outlines
Textbook and Lab Manuals
Materials 2024-2025

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

WeekVolume 1 Lab (Thursday)Volume 2 Lab (Tuesday)
1**UNIX Shell 1LABOR DAY
2*Standard LibraryIntro to Python
3*OOPNumPy
4*Exceptions/IOMatPlotLib
5Linear Transformations*Unit Testing
6Linear SystemsSLACK DAY
7SLACK DAYBSTs
8The QR DecompositionNearest Neighbors
9Least Squares and Computing EigenvaluesBreadthFirstSearch
10Image SegmentationDijkstra (New)
11The SVD and Image CompressionMarkov chains
12Facial Recognition using EigenfacesSampling Lab
13THANKSGIVINGDFT
14**SQL 1Convolution
15NONENONE

Second Semester

WeekVolume 1 Lab (Tuesday)Volume 2 Lab (Thursday)
1No ClassWaveletes
2**SQL 2Polynomial Interpolation
3*Introduction to SymPyGaussian Quadrature
4DifferentiationLine Search
5Conditioning and StabilitySLACK DAY
6Monte Carlo Integration**Regular Expressions
7Visualizing Complex-valued FunctionsGradient Descent Methods
8President's DayThe Simplex Method
9The PageRank AlgorithmGymnasium
10*ProfilingConvex opt
11SLACK DAYNon-negative Matrix Factorization
12*Data VisualizationInterior Point 1: Linear Programs
13**UNIX Shell 2Dynamic Programming
14Iterative SolversPolicy Function Iteration
15REVIEWREVIEW

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

WeekVolume 3 (Tuesday)Volume 4 (Thursday)
1Labor DayAnimation
2Pandas 1: IntroIntro to IVP and BVP Solvers
3Pandas 2: PlottingSIR
4Pandas 3: Grouping and Pivot TablesIVP
5Info + WordleSLACK DAY
6Pandas 4: GeopandasPredator-Prey
7SKLearn and LSILorenz
8Data Cleaning and FEBifurcation
9RF and Decision TreesFinite Difference
10SLACK DAYHeat Eqn
11KmeansWave Eqn
12OLSAnisotropic
13ParallelTHANKSGIVING
14Logistic RegressionFinite Element
15SLACK DAYSLACK DAY

Second Semester

WeekVolume 3 (Tuesday)Volume 4 (Thursday
1Naive BayesPoisson's Equation
2Choose one( Apache Spark, Parallel Programming with MPI, Web Scraping or, Web Crawling) Spectral 1: Method of Mean Weighted Residuals
3Metropolis AlgorithmSpectral 2: A Pseudospectral Method for Periodic Functions
4Gibbs Sampling and LDAInverse Problems
5Gaussian Mixture ModelsThe Shooting Method for Boundary Value Problems
6Discrete Hidden Markov ModelsTotal Variation and Image Processing
7President's DayTransit Time Crossing a River
8Speech Recognition Using CDHMMsHIV Treatment Using Optimal Control
9Kalman FilterSolitons
10ARMA ModelsSLACK DAY
11Project DayObstacle Avoidance
12Non-Negative Matrix Factorization RecommenderThe Inverted Pendulum
13Deep LearningTimber Harvesting
14Recurrent Neural NetworksProject Day