Maria K. Cameron

University of Maryland, Department of Mathematics


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AMSC 808N/CMSC828V: Numerical Methods for Data Science and Machine Learning

Fall 2020
Instructor: Maria Cameron

A brief description: Optimization (fundamentals of constrained and unconstrained optimization, algorithms for large-scale problems, Tikhonov and lasso regularization). Matrix data and latent factor models (Ky-Fan norms, nonlinear matrix factorization, CUR decomposition, applications). Dimensionality reduction for data visualization and organization (PCA, MDS, isomap, LLE, t-SNE, diffusion maps). Graph data analysis (basic graph algorithms (DFS and BFS), random graph models, site and edge percolation, mining large graphs).

Expectations: The students are expected to have solid knowledge of linear algebra and multivariable calculus and be able to program.

Coursework:

Slides: Intro.pdf
Lecture Notes: 1-Introduction.pdf
Slides: ClassificationIntro.pdf
Lecture Notes: 2-Optimization.pdf
Homework: hw1.pdf; hw2.pdf, HW2.zip; hw3.pdf, HW3.zip
Project: project1.pdf, Project1.zip
Lecture Notes: 3-MatrixFactorization.pdf
Homework: hw4.pdf
Project: project2.pdf, Project2.zip
Lecture Notes: 4-DimReduction.pdf
Homework: hw5.pdf, HW5.zip
Slides: NetworksIntro.pdf, NetworksProcesses.pdf MiningLargeGraphs.pdf
Lecture Notes: 5-GraphDataAnalysis.pdf
Homework: hw6.pdf

Take-home final exam: problem 1, problem 2.

Some key references: