Combined B.S./M.S. Computer Engineering and Artificial Intelligence Degrees

Program type:

Major

Grad Track

Format:

On Campus

Est. time to complete:

5-6 years

Credit Hours:

153

Accelerate your education with UNT and dive into deep learning, machine learning, data science and feature engineering.

The Bachelor of Science with a major in computer engineering provides a high-quality education by balancing the theoretical and experimental aspects of hardware and software issues. A BS with a major in computer engineering provides excellent job prospects in the engineering and technology sectors. Considering a career in the exciting field of artificial intelligence? UNT Engineering’s graduate track program will help you transition into graduate coursework while working on your undergraduate degree in Computer Engineering. Our Artificial Intelligence program offers the only standalone Master of Science in Artificial Intelligence in the state of Texas and is one of few nationwide. UNT’s degree is interdisciplinary, allowing students to leverage their existing skillset and experience by combining it with AI knowledge. Graduate students in our program have the opportunity to specialize in machine learning, biomedical engineering, or autonomous systems.

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Why earn degrees in Computer Engineering and Artificial Intelligence?

The Department of Computer Science and Engineering provides high quality educational programs by maintaining a balance between theoretical and experimental aspects of computer science, as well as a balance between software and hardware issues, and by providing curricula that serve the citizens and industrial organizations of Texas in general, and those in North Texas in particular.

The graduate track program allows students to take graduate coursework while earning their Bachelor's so they can save time and money on their education, and transition into their graduate studies so they can achieve their career goals sooner.

The AI program is designed to meet the needs of our students and industry; students will graduate with marketable skills in programming, data analysis, problem solving, and communication. Dive into deep learning, machine learning, big data and data science, and feature engineering.

Concentration areas focus on machine learning, biomedical engineering, and autonomous systems, offering students the opportunity to choose their own career path.

Students who graduate from this program will:

Understand and apply the concepts of programming related to AI
Identify and implement AI applications to solve problems
Create and utilize AI systems that respond to market needs
Analyze and apply AI concepts and applications available in their chosen field of interest
Understand the business needs and job market in AI

The College of Engineering has state-of-the-art instructional facilities and laboratories containing cutting-edge research equipment. Top-ranked research labs offer exciting possibilities for study and discovery. Alongside faculty members, you’ll explore areas of study that will prepare you for the most in-demand AI jobs.

Marketable Skills

Analyze the complexity of an algorithm
Use digital logic in circuit design
Compare merits of computer architectures
Teamwork
Learn a programming language
Code using AI programming skills
Design, collect, and analyze data
Solve problems with creative solutions
Quickly grasp new concepts
Collaborate and communicate in teams

Computer Engineering and Artificial Intelligence Highlights

Research lab space includes space for the Centers for Computational Epidemiology and Response Analysis, Information and Cybersecurity, and Net-Centric Software, as well as 13 research labs dedicated to faculty and student research.

Current research interests of the faculty include theoretical computer science, databases, visualization, game programming, wired and wireless networks, computer security, artificial intelligence, natural language processing, computer systems architecture, agent based systems, collaborative learning, parallel and distributed processing, and numerical analysis.

UNT is located in Denton, TX, a college town that part of the Dallas-Fort Worth metropolitan area. DFW is the country’s fourth-largest metropolitan area and offers a wealth of job opportunities as well as entertainment, culture, and more.

Our Living Learning Community brings together engineering students who live on campus to enhance academic and social experiences.

Teaching and research assistantships provide support for many graduate students. In addition to a monthly stipend, assistantships also qualify students for in-state tuition rates, and many students receive tuition and fee support. The College of Engineering also offers scholarships to qualified students throughout the year.

The Bachelor of Science degree with a major in computer engineering is accredited by the Engineering Accreditation Commission (EAC) of ABET.

What Can You Do With Degrees in Computer Engineering and Artificial Intelligence?

The most in-demand jobs are data scientists, software engineers, and machine learning engineers, but career opportunities in artificial intelligence can span a wide array of disciplines. If you envision yourself in the healthcare industry, then a concentration in biomedical engineering could be perfect. If robotics, self-driving vehicles, or drones are more your speed, then a path in machine learning or autonomous systems could open an exciting field for you. Whichever route you choose, the marketable skills you’ll receive from this program are sure to prepare you for a challenging career in AI.

Computer Engineering and Artificial Intelligence Courses You Could Take

Introduction to the concepts of analysis and design of communication system components using signal analysis techniques. Amplitude and angle modulation for the transmission of continuous-time signals. Introduction to analog and digital filter design and analysis.

Elementary concepts of continuous-time and discrete-time signals and systems. Specific topics include linear time-invariant (LTI) systems, impulse response, convolution, Fourier series, Fourier transforms, frequency-domain analysis of LTI systems, Laplace transforms, z-transforms, and rational function descriptions of LTI systems.

New programming paradigms are needed to create and manage systems with embedded AI capabilities. Students are taught how to leverage available artificial intelligence APIs flexibly and reliably. Additionally, as data management is integral to AI system development, an emphasis is made to collect and process data for AI system training and testing.

Focused on applied signal processing across a variety of modalities including still images and video, audio signals, sensor signals, and natural extensions to other rich multidimensional signals. Students are introduced to computer and simplified biological visual and auditory processing models. This includes signal transduction, edge detection and segmentation. Spectral representations include fourier and various wavelet decompositions useful in making inferences from signals.

Study of the fundamentals of pattern recognition techniques including Bayesian decision and estimation, non-parametric methods, multi-class classifiers and feature selection methods.

Introduction to contemporary electronic devices, terminal characteristics of active semiconductor devices, and models of the BJT and MOSFET in cutoff and saturation region are introduced. Incremental and DC models of junction diodes, bipolar transistors (BJTs), and metal-oxide semiconductor field effect transistors (MOSFETs) are studied to design single and multistage amplifiers.