Electrical & Computer Engineering > Graduate > Certificates > Machine Learning for Engineers and Scientists

Professional Certificate: Machine Learning for Engineers and Scientists

Advance your career with the professional certificate of
Machine Learning for Engineers and Scientists
Offered by the UTD Electrical and Computer Engineering Department

Machine learning techniques are being increasingly used in all engineering and science fields as these techniques have proven effective in solving various engineering and science problems. The professional certificate of Machine Learning for Engineers and Scientists is designed to introduce industry professionals and project managers to basic concepts in machine learning for staying updated with AI-based solutions that are growingly being deployed in industry. Obtaining this certificate enables industry professionals with no or limited prior knowledge of machine learning to remain more competitive in the job market.

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Certificate Overview

The certificate consists of three machine learning modules: Fundamentals of Machine Learning, Applied Machine Learning, and Applications of Machine Learning in Engineering and Sciences. The first module introduces the background and fundamental concepts of devising a machine learning solution. The second module discusses machine learning techniques which are widely used and goes through example Python codes for implementing them. The third module includes a cross section of different machine learning models in engineering and science applications such as healthcare, energy, manufacturing, transportation, finance, etc.

Certificate Details

Fundamentals of Machine Learning

This module introduces the fundamental concepts that are used in devising a machine learning solution. An overview of the following basic mathematical concepts in machine learning will be covered.

  1. Data paradigms and dimensionality reduction
  2. Basic concept of loss function and optimization in machine learning
  3. Basic concept of probability formulation and parametric/non-parametric density estimation in machine learning
  4. Basic concept of linear and logistic regression
  5. Supervised and unsupervised learning
  6. Evaluation metrics for assessing a machine learning solution
Applied Machine Learning

This module covers the widely used machine learning techniques listed below and discusses example Python codes for implementing them. Prewritten Python codes will be explained for the experiments listed below discussing what step each part of the codes implements. In the experiments, the focus will be placed on preparing data, dividing data into training and testing sets, choosing a suitable machine learning model, training the model based on training data, and evaluating the model based on testing data.

  1. Data preprocessing and conditioning
  2. Bayesian decision making
  3. Linear classifiers
  4. Data-driven neural network learning
  5. Deep learning/convolutional neural network
  6. Unsupervised learning and clustering
Applications of Machine Learning in Engineering and Sciences

This module covers a cross section of engineering and science applications related to healthcare, energy, manufacturing, transportation, finance, and social media, involving different machine learning models. Example applications include, but are not limited to, the following:

  1. Application of machine learning in healthcare for disease diagnosis
  2. Application of machine learning in energy for power forecasting
  3. Application of machine learning in manufacturing for machine vision inspection
  4. Application of machine learning in transportation for navigation guidance
  5. Application of machine learning in finance for credit scoring
  6. Application of machine learning in social media for interest clustering

When and How is the Certificate Offered

This certificate is scheduled to be offered in a flexible mode over nine Saturdays 9am-12pm in Summer 2025 from June 7th through August 2nd. Its flexible mode allows attending the Saturday sessions either in-person at UTD or virtually online. At least seven out of the nine sessions need to be attended either in-person or virtually in order to receive the certificate. The requirement for registration is having at least a BS degree in any engineering or science field.

Registration and Fee

Complete and submit the registration form at this link. Once approved, you are sent a link to pay the registration fee of $2,950. Then, further logistics information will be emailed to you. For a group of five or more registrations from the same organization or company, the fee per registration is discounted to $2,450. The offering of the certificate will only proceed when there are at least 10 paid registrations by May 1. In case there are fewer than 10 registrations, the registration fee will be refunded.

Register

Certificate Instructors

The instructors of the certificate will be the following three UTD Professors of Electrical and Computer Engineering with a combined professional experience of over 100 years in teaching and research: Dr. Kiasaleh, Dr. Kehtarnavaz, and Dr. Tamil.