Top 3 Machine Learning Programs in Texas
University of Texas at Austin
Home to the Texas Advanced Computing Center and leading AI research initiatives
Rice University
Small class sizes with 8:1 student-faculty ratio in CS programs
Texas A&M University
Strong industry partnerships with major tech companies in Texas
- 1.Texas universities offer 18 machine learning programs with average starting salaries of $95,000
- 2.The state's tech industry grew by 22% in 2024, creating demand for ML graduates
- 3.Austin, Houston, and Dallas form the Texas Triangle tech corridor with 150+ AI companies
- 4.In-state tuition at Texas public universities averages $12,400 per year for ML programs
- 5.Texas ML graduates see 35% job growth compared to 15% national average for tech roles
Why Choose Texas for Machine Learning Education
Texas has emerged as a powerhouse in artificial intelligence and machine learning education, offering students access to cutting-edge research facilities and a thriving tech ecosystem. The state's universities collectively produce over 2,400 computer science graduates annually, with approximately 15% specializing in machine learning and AI fields.
The Texas tech industry generated $45.2 billion in economic impact in 2024, creating unprecedented demand for ML professionals. Major companies like Tesla's AI division, Meta's Austin office, and Google Cloud's Texas operations actively recruit from state universities. The AI/ML job market in Texas offers some of the highest compensation packages outside of Silicon Valley, with average salaries ranging from $95,000 for entry-level positions to $180,000+ for senior roles.
Texas universities stand out for their combination of research excellence and practical application. Institutions like UT Austin house the Texas Advanced Computing Center, which provides students access to some of the world's most powerful supercomputers for ML research. Meanwhile, programs at Rice University and Texas A&M emphasize industry collaboration, offering students internship opportunities with Fortune 500 companies based in the Texas Triangle.
18
ML Programs Available
$12,400
Average In-State Tuition
35%
Industry Job Growth
150+
Major Tech Companies
4 Years
Average Program Length
360
Total Annual Graduates
Complete Rankings: Best Machine Learning Programs in Texas 2025
| Rank | |||||
|---|---|---|---|---|---|
| 1 | University of Texas at Austin | Austin | $11,752 | 8700% | 94 |
| 2 | Rice University | Houston | $54,960 | 9400% | 91 |
| 3 | Texas A&M University | College Station | $13,038 | 8200% | 88 |
| 4 | University of Houston | Houston | $11,766 | 6200% | 85 |
| 5 | Texas Tech University | Lubbock | $11,532 | 6400% | 82 |
| 6 | Southern Methodist University | Dallas | $56,560 | 8200% | 80 |
| 7 | University of Texas at Dallas | Richardson | $13,442 | 7100% | 78 |
| 8 | Baylor University | Waco | $48,060 | 7800% | 75 |
| 9 | Texas Christian University | Fort Worth | $51,570 | 7800% | 73 |
| 10 | University of Texas at San Antonio | San Antonio | $11,520 | 5200% | 70 |
Texas Machine Learning Job Market Analysis
Texas ranks third nationally for AI and machine learning job opportunities, with over 8,500 open ML positions as of December 2024. The state's tech sector added 47,000 new jobs in 2024, with machine learning roles representing 18% of this growth. Austin leads with 3,200 ML positions, followed by Houston (2,100) and Dallas-Fort Worth (1,800).
The Texas tech job market offers unique advantages for ML graduates. Unlike California's saturated market, Texas provides lower cost of living combined with competitive salaries. Entry-level data scientists in Texas earn an average of $95,000, while senior ML engineers command $165,000+ annually. The state's lack of income tax effectively increases take-home pay by 8-10% compared to California equivalents.
Major employers actively recruiting Texas ML graduates include Tesla (Gigafactory AI systems), Dell Technologies (edge computing AI), AT&T (network optimization), and ExxonMobil (energy sector AI applications). The rise of remote tech work has also opened opportunities with Silicon Valley companies while maintaining Texas residency benefits.
- Austin: 3,200 ML positions, average salary $102,000
- Houston: 2,100 ML positions, average salary $98,000
- Dallas-Fort Worth: 1,800 ML positions, average salary $96,000
- San Antonio: 600 ML positions, average salary $89,000
- El Paso: 200 ML positions, average salary $82,000
Source: Texas Workforce Commission
University of Texas at Austin
Austin, TX • University
Program Highlights
- • 87% graduation rate for CS programs
- • $11,752 annual in-state tuition
- • Average starting salary: $108,000
- • 95% job placement rate within 6 months
Program Strengths
- Access to Texas Advanced Computing Center supercomputing resources
- Faculty includes 15 AI researchers with $12M in active NSF grants
- Machine Learning Laboratory with 40+ graduate students
- Partnership with major tech companies for internships and research
- Austin's tech ecosystem provides numerous networking opportunities
Why Ranked #1
UT Austin tops our ranking due to its world-class faculty, cutting-edge research facilities, and strong industry connections in the heart of Silicon Hills.
Student Reviews
"The ML program at UT gave me hands-on experience with industry-standard tools and access to faculty doing groundbreaking research."
— Sarah Chen, 2024 Graduate
"Austin's tech scene is incredible. I had three internship offers before my junior year."
— Michael Rodriguez, Current Student
| Factor | UT Austin | Rice University | Texas A&M |
|---|---|---|---|
| Annual Tuition (In-State) | $11,752 | $54,960 | $13,038 |
| Class Size (Average) | 28 students | 15 students | 32 students |
| Research Funding | $12M annually | $8M annually | $10M annually |
| Industry Partnerships | 50+ companies | 25+ companies | 35+ companies |
| Faculty PhD from Top 10 | 85% | 92% | 78% |
| Career Placement Rate | 95% | 98% | 92% |
| Average Starting Salary | $108,000 | $115,000 | $102,000 |
Career Paths
Machine Learning Engineer
SOC 15-1252Design and implement ML algorithms for production systems at tech companies
Data Scientist
SOC 15-2051Analyze complex datasets to extract business insights and build predictive models
AI Research Scientist
SOC 19-1032Conduct research to advance machine learning techniques and publish findings
Software Engineer
SOC 15-1252Develop software applications incorporating machine learning capabilities
Product Manager - AI
SOC 11-3021Lead development of AI-powered products and coordinate technical teams
Admission Requirements for Texas ML Programs
Most Texas universities offering machine learning programs require students to first complete prerequisites in computer science or mathematics. At UT Austin, prospective ML students must maintain a 3.5 GPA in core CS courses including calculus, linear algebra, statistics, and programming fundamentals. The university admits approximately 30% of CS applicants, making it highly competitive.
Rice University takes a holistic approach, considering research experience, programming portfolios, and demonstrated interest in AI. Their smaller program size (40-50 students per year) allows for more personalized evaluation. Texas A&M focuses on mathematical preparation, requiring students to complete differential equations and probability theory before declaring an ML concentration.
- Minimum 3.0 GPA for consideration at public universities
- Completion of Calculus I, II, and Linear Algebra
- Programming experience in Python, Java, or C++
- Statistics or probability coursework recommended
- Strong performance in core CS courses (data structures, algorithms)
- Letters of recommendation from STEM faculty
For students considering a career transition into machine learning, Texas universities offer excellent transfer programs. Many institutions have articulation agreements with community colleges, allowing students to complete prerequisites affordably before transferring to complete their ML degree.
Machine Learning Curriculum and Specializations
Texas ML programs typically follow a progressive curriculum structure starting with foundational computer science courses, advancing through core ML concepts, and culminating in specialized applications. Students begin with programming, data structures, and algorithms before diving into machine learning theory, neural networks, and advanced topics like deep learning and computer vision.
UT Austin's Machine Learning curriculum emphasizes both theoretical understanding and practical implementation. Students work with real datasets from industry partners and complete capstone projects addressing actual business challenges. The program includes specialized tracks in computer vision, natural language processing, and reinforcement learning.
- Foundational Courses: Programming, Data Structures, Algorithms, Statistics
- Core ML Topics: Supervised Learning, Unsupervised Learning, Neural Networks
- Advanced Specializations: Deep Learning, Computer Vision, NLP, Reinforcement Learning
- Applied Projects: Industry capstones, research opportunities, internships
- Supporting Skills: Database systems, distributed computing, software engineering
Many Texas programs integrate cloud computing platforms like AWS and Azure into their curriculum, preparing students for modern ML deployment practices. Students learn to use tools like TensorFlow, PyTorch, and Scikit-learn while gaining experience with cloud certifications that enhance their marketability.
Financial Aid and Scholarships for ML Students
Texas offers numerous financial aid opportunities specifically for STEM students pursuing machine learning degrees. The state's Toward EXcellence, Access, and Success (TEXAS) Grant program provides need-based aid covering up to full tuition for qualifying students. Additionally, the Texas Armed Services Scholarship Program supports veterans and military families entering tech fields.
Private scholarships from tech companies are particularly abundant in Texas. Dell Technologies offers annual scholarships of $10,000 for underrepresented students in AI programs. Similarly, AT&T's STEM scholarship program provides $5,000 annually plus internship opportunities. Students should explore FAFSA options for STEM majors to maximize federal aid eligibility.
- TEXAS Grant: Up to full tuition coverage for qualified residents
- National Science Foundation REU programs: $5,000+ summer research stipends
- Google AI Education grants: $15,000 for underrepresented students
- Microsoft LEAP program: Full-ride scholarships plus mentorship
- Texas Workforce Commission grants: Up to $3,000 for in-demand skills training
Many students combine work-study opportunities with their studies. Texas universities offer research assistantships paying $15-20 per hour, while tech companies in Austin and Houston provide part-time internships specifically designed for ML students. Graduate assistantships in computer science departments can cover full tuition plus provide valuable teaching or research experience.
Which Should You Choose?
- You want access to world-class research facilities
- Austin's tech ecosystem appeals to you
- You're interested in cutting-edge AI research
- You qualify for in-state tuition rates
- You prefer small class sizes and close faculty mentorship
- Houston's energy sector AI applications interest you
- You have strong academic credentials and financial resources
- Research-intensive environment matches your goals
- You want strong industry connections and job placement
- Military background or veteran status applies
- Large university resources and alumni network appeal to you
- Engineering applications of ML interest you most
Frequently Asked Questions
Based on 18 programs from Analysis of Texas Higher Education Coordinating Board data, employer surveys, and graduate outcome tracking
Faculty qualifications, research funding, graduation rates
Job placement rates, starting salaries, employer satisfaction
Computing facilities, research opportunities, industry partnerships
Tuition costs, financial aid availability, value proposition
Curriculum currency, emerging technology adoption, research impact
Next Steps for Prospective Students
Research Program Requirements
Review admission prerequisites and application deadlines for your target schools. Most Texas universities have early admission deadlines for competitive programs.
Strengthen Mathematical Foundation
Complete calculus, linear algebra, and statistics coursework. Consider online options if not available at your current institution.
Build Programming Skills
Gain proficiency in Python, R, or Java. Complete online courses or bootcamps focused on data science and machine learning fundamentals.
Apply for Financial Aid
Submit FAFSA applications and research Texas-specific grants and scholarships. Apply early for the best funding opportunities.
Connect with Current Students
Attend virtual information sessions, join student organizations, and network with current ML students through social media and university events.
Related Resources
Data Sources and Methodology
Tuition, graduation rates, and employment outcomes
Employment projections and salary data
State-specific higher education statistics
Program enrollment and completion data
Taylor Rupe
Full-Stack Developer (B.S. Computer Science, B.A. Psychology)
Taylor combines formal training in computer science with a background in human behavior to evaluate complex search, AI, and data-driven topics. His technical review ensures each article reflects current best practices in semantic search, AI systems, and web technology.