The University of Texas at Arlington

How this program compares

Benchmark this program against our national recognition pages and use the key guides below to evaluate ROI, admissions difficulty, and outcomes.

Admission Snapshot

Typical admitted student: A bachelor's degree in computer science, engineering, mathematics, or a related field with a minimum GPA of 3.0 is required; relevant programming or quantitative experience is often preferred.

About This Program

This credit-bearing program provides a rigorous foundation in core AI methodologies, focusing on search, optimization, and probabilistic models for real-world application.

Career Outcomes

Gain machine learning skills to advance as Machine Learning Engineer in Engineering AI

• 1. AI/ML Engineer
• 2. Data Scientist
• 3. AI Ethics Consultant
• 4. Machine Learning Specialist

What You'll Learn

• Implement machine learning algorithms for predictive modeling
• Design and train deep neural networks for complex data
• Apply natural language processing to text analysis tasks
• Develop computer vision systems for image recognition
• Evaluate AI systems for ethical biases and fairness

Curriculum Highlights

The curriculum covers key topics such as knowledge representation, neural networks, and machine learning through four or five graduate-level courses.

Top Employers

Top employers include tech giants like Google, Amazon, Microsoft, and IBM, as well as consulting firms and research organizations in AI applications.

Admissions

A bachelor's degree in computer science, engineering, mathematics, or a related field with a minimum GPA of 3.0 is required; relevant programming or quantitative experience is often preferred.

Application Materials

• Statement of Purpose: Required
• Letters of Recommendation: 2–3
• Resume: Required
• Transcripts: Official transcripts required

Academic Requirements

• Degree Required: Bachelor's degree
• GRE/GMAT: Not Required
• TOEFL/IELTS: Required for international students (TOEFL 80+ / IELTS 6.5+)