Online Master of Engineering Program

Duration: 8 Weeks

Credit Hours: 4

Overview of the legal system. Legal principles affecting the engineering profession. Professional ethics in engineering. Intellectual property law. Basic contract and tort principles. Environmental law.

Duration: 8 Weeks

Credit Hours: 4

This course will cover the project lifecycle for successful and effective managing of engineering projects in accordance with the theories, insights, and principles of the Project Management Body of Knowledge. The course will elaborate on understanding the sophisticated dynamics of Engineering Management through expanding on project constraints, such as scope, cost, time, risk, quality, and so forth as well as providing details on the means and methods to enable engineering managers to attain a better and multi-dimensional understanding of the project lifecycle, and the necessary skill sets to efficiently manage the engineering enterprise.

Duration: 8 weeks

Credit hours: 4

MENG 404 sets the stage for the artificial intelligence and machine learning topics that students will see throughout the Master of Engineering program. While recalling important mathematical and statistical topics, MENG 404 will also familiarize students with Python which has become an industry standard for artificial intelligence and machine learning calculations. Students will be introduced to applications where the material will be covered more thoroughly and further applied in their upcoming MENG courses.

Duration: 8 weeks

Credit hours: 4

Working engineers need to know about Innovation Tools & Methods because these are the interdisciplinary business techniques that ensure that the problems we choose to solve and the products we make to solve them actually meet the needs of the human beings who use them (and thus achieve success in the marketplace). Most engineers have heard about the importance of figuring out what people actually need, but this course teaches the concrete techniques that are currently accepted best practices for doing so.

Duration: 8 weeks

Credit hours: 4

This course will focus on image formation, geometry and stereo. Two-dimensional image analysis by fourier and other 2-D transforms. Image enhancement, color, image segmentation, compression, feature extraction, object recognition.

Duration: 8 weeks

Credit hours: 4

This course will introduce students to Deep Neural Networks (DNN). DNN are the leading pattern recognition methods in AI. They are very successful at correctly classifying a wide range of input data, including images, video, speech, text, and they successfully model consumer preferences. We now use DNNs in our daily life ranging from automated telephone systems to user recommendations on online shopping and entertainment applications (Amazon, Netflix). DNNs can also automatically generate graphics, natural looking images, natural sounding speech and text.

Duration: 8 weeks

Credit hours: 4

CS 411 will provide students with experience with a range of AI tools including search, reinforcement learning, probabilistic models, and game theory with the goals of understanding their strengths and limitations and being able to apply then to problems they may face during their career.

Duration: 8 weeks

Credit hours: 4

This course gives an introduction to common machine learning tasks (e.g., classification, density estimation, clustering) and some of the successful machine learning techniques and broader paradigms that have been developed for these tasks. The course is programming-intensive and a large emphasis will be placed on tying existing machine learning techniques to specific real-world applications through hands-on experience. By taking the course, students will be able to explain the mechanism of different common machine learning task settings and be able to apply existing machine learning tools to a broad range of data sets. They will also be able to identify appropriate problem formulations and machine learning techniques for framing and solving predictive data analysis problems, along with evaluating and measuring the performance of different learning techniques on predictive tasks.

Duration: 8 weeks

Credit hours: 4

In the class, we will study some background theory and focus on engineering techniques and libraries to allow computers to interact with humans in a natural language. We will also cover topics such as extracting facts and concepts from text, and other applications of natural language processing to mainstream applications.

• Completed online application
• Resume
• Two reference letters
• A non-refundable application fee of $70.00 upon application submission. International applicants pay an additional fee.
• 3.0 GPA on a 4.0 scale.
• Two years or more of post-bachelor’s work experience in engineering, computer science or a related field.
• Registrar-issued transcripts (copies) from all colleges or universities attended.
  • International students refer to: International Requirements
• A strong academic interest in engineering and an enthusiasm for online learning in a collaborative virtual community of faculty and fellow students.
• GRE scores are NOT required for admission to the online Master of Engineering program.

Applicants whose native language is not English are required to take an English competency test.

Minimum required scores are:

• iBT Internet-based TOEFL of 80, with subscores of Reading 19, Listening 17, Speaking 20, and Writing 21;
• New Paper-Based TOEFL (after August 2018) of 60, with subscores of Reading 19, Listening 17, Writing 21;
• Institutional Testing Paper-Based TOEFL (prior to August 2018) of 550;
• IELTS of 6.5, with all four subsections of at least 6.0; or PTE-Academic of 54, with subscores of Reading 51, Listening 47, Speaking 53, and Writing 56.