Teaching at UTSA
ME 6973 Introduction to Deep Learning

Introduction to Neural Networks and Deep Learning

Basic Neural Networks

Neural Networks Optimization

Convolutional Neural Networks

Recurrent Neural Networks

Sequence Models & Attention Mechanism

Graph Neural Networks

Generative Adversarial Networks

Deep Reinforcement Learning

Practical Aspects of Deep Learning
ME 6973 Adv Reliability Methods

Extreme value theory

Random fields

Samplingbased reliability analysis methods including Monte Carlo simulation

Response surface development

Random processes

System reliability
ME 6543 Machine Learning and Data Analytics

Parametric regression: Linear/polynomial regression

Optimization: Gradient descent algorithm

Variable selection: Regularization and crossvalidation

Nonparametric regression: Knearest neighbor, Gaussian processes

Classification basics: Loss functions, logistic regression, decision trees

Classification advanced: Support vector machines

Ensemble methods: Boosting, bagging, random forest

Dimension reduction: Principal component analysis

Clustering: Kmeans clustering

Deep learning: Feedforward neural networks, convolutional neural networks

Reinforcement Learning: Markov decision process
ME 5013  Advanced Data Analytics

Data visualization: Multivariate, hierarchical, temporal, and network data visualization

Regression and regularization: Linear regression, logistic regression, regularization, ridge regression, nonparametric regression with Gaussian processes

Classification basics: Loss functions, naive Bayes, linear classifiers

Support vector machines, convex optimization

Kernels: Model selection, cross validation

Ensemble methods: Boosting, bagging, random forest

Dimension reduction: principal component analysis

Clustering, mixture models, EM algorithms

Bayesian Inference, sampling algorithms, MCMC

Stochastic processes: Markov models, hidden Markov models

Graphical Models: State space models, Kalman filter
EGR 5213 Introduction to Modelling and Simulation

Preliminaries

Random variable generation

Markov Chain


Monte Carlo Methods

Monte Carlo Integration

Monte Carlo Optimization

Markov Chain Monte Carlo

Convergence


Discrete Event Simulation

Simulation concepts

Modeling basic operations and inputs

Statistical analysis of output

Continuous and combined models


Simulation Software

Excel

MATLAB/SIMULINK

ARENA

ME 4723 – Reliability and Quality Control

Reliability concepts

Probability and life distribution for reliability analysis

Design for six sigma

Product development

Failure modes, mechanisms, and effect analysis

Probabilistic design for reliability and the factor of safety

Reliability estimation techniques

Process control and process capability

Analyzing product failures and root causes

System reliability modeling

Warranty analysis
ME 3263 – Manufacturing Engineering

Introduction to Manufacturing Engineering: Products, Processes, and Systems

Manufacturing Systems: An Overview of Basics

Mechanical Properties of Materials

Tolerances

Measurement and Quality Assurance

Manufacturing Processes
EGR 2323 – Applied Engineering Analysis I

Mathematical modeling of engineering problems

Separable ODE

Integrating factors

First, second, and higherorder linear constant coefficient ODE’s

Nonhomogeneous ODE

Laplace Transforms

s and t translation

Convolution Solution of an ODE via Laplace transform

Existence and uniqueness of solution to a system of linear algebraic equations

Gauss elimination and rank

Determinant, Cramer’s rule, and inverse of a matrix

Eigenvalues and eigenvectors

Diagonalization

Solutions to system of ODE