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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

  • Sampling-based 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 cross-validation

  • Nonparametric regression: K-nearest 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: K-means 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


    • 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 higher-order linear constant coefficient ODE’s

  • Non-homogeneous 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

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