Topic 1: Introduction to Machine Learning and Data Sciences. Focus on algorithms, mathematics, implementations (on Scikit-learn), and real-life applications of machine learning and data sciences including machine learning algorithms such as supervised learnings (linear models, support vector machines, kernel methods, tree-based algorithms), unsupervised learnings (Density estimation, clustering, dimensionality reduction, PCA), and deep neural networks. Explore the foundations of machine learning including optimization, probability, advanced linear algebra, statistics, and methods to evaluate machine learning methods. Computational Engineering 379L (Topic: Intro to Machine Learning) and 379L (Topic 1) may not both be counted. Additional prerequisite: Computational Engineering 322, and Mathematics 427J or 340L with a grade of at least C- in each.

Topic 2: Simulation-based Aerodynamic Design and Analysis. Computational Engineering 379L (Topic: Simulatn Aerodynmc Dsgn/Anlys) and 379L (Topic 2) may not both be counted.

Topic 3: Computational Methods. Examine basics of vectors and matrices, linear equations, regression and classification, similarity measures, the Discrete Fourier Transform (DFT), linear filters, and power spectrum estimates. Focus on applying matrix methods to practical applications, such as tomography, image processing, data fitting, time series prediction, optimal control, finance, and machine learning. Utilize MATLAB or Python to do computations with vectors and matrices and run numerical experiments with real-world data sets. Computational Engineering 379L (Topic: Computational Methods) and 379L (Topic 3) may not both be counted. Additional prerequisite: Mathematics 408D, 408L, or 408M and Computational Engineering 301 with a grade of at least C- in each.

Topic 4: Fundamentals and Geophysical Applications of Imaging Radar Systems. Explore how radar images are formed and manipulated, as well as applications of the systems to problems such as measurement of the Earth crustal deformation. Focus on radar as a signal processing problem, radar image formation and radar interferometry. Discuss system design, scattering from natural surfaces, range and azimuth processing algorithms, and processor design. Computational Engineering 379L (Topic: Fund/Geophy App Imag Radar Sys) and 379L (Topic 4) may not both be counted.

Topic 5: Biomechanics of Tissues, Scaffolds, and Cells. Computational Engineering 379L (Topic: Tissue/Scaffold/Cell Biomch) and 379L (Topic 5) may not both be counted.

Topic 6: Software Design for Responsible Intelligent Systems. Discuss the design, implementation, operation, and assurance of intelligent software systems based on data intensive computing and machine learning techniques. Utilize real-world datasets from engineering disciplines. Computational Engineering 379L (Topic: Sw Dsgn For Resp Intellgnt Sys) and 379L (Topic 6) may not both be counted. Additional prerequisite: Computational Engineering 332 with a grade of at least C-.

Topic 7: Parallel Computing for Science and Engineering. Explore fundamentals of parallel programming for scientific and engineering applications. Examine the study of general parallel computing principles, including asymptotics; current computer architectures and how parallelism manifests itself in them. Computational Engineering 379L (Topic: Parallel Computng For Sci/Engr) and 379L (Topic 7) may not both be counted. Additional prerequisite: Mathematics 408M or 408D and Computational Engineering 322 with a grade of at least C-.