College of Engineering at Texas A&M University
Online Artificial Intelligence and Machine Learning CertificateOnline
DURATION
12 Hours
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
Request application deadline *
EARLIEST START DATE
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TUITION FEES
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STUDY FORMAT
Distance Learning
* spring 2025
Key Summary
Introduction
Built for working professionals, this graduate-level Certificate in Artificial Intelligence and Machine Learning will help you to gain a competitive edge. Whether you're just starting out or a seasoned pro, this program provides essential skills to harness the full potential of AI and ML in your field.
Upon completion, you'll master statistical analysis and machine learning techniques, unravelling complex datasets. Armed with the ability to create and evaluate AI models, you'll confidently tackle real-world challenges. Utilize cutting-edge tools to uncover actionable insights and drive innovation in your industry.
Why choose Engineering Online
Advance your career with our Engineering Online program! Backed by the university's esteemed reputation and national recognition in engineering education, you'll engage directly with industry leaders and a rigorous curriculum. Beyond graduation, tap into the extensive Aggie Alumni Network, offering invaluable connections to propel your career forward.
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Ideal Students
Please note: This certificate is intended for working professionals and is not open to current Texas A&M students.
Technical Qualifications
To be successful in this program, prospective students must demonstrate an understanding of core concepts in computer science or equivalent covered in the categories below:
- Program Design and Concepts: programming proficiency through problem-solving with a high-level programming language, emphasizing computational thinking, data types, object-oriented design, dynamic memory management, and error handling for robust program development.
- Data Structures: implementing essential abstract data types and algorithms covering stacks, queues, sorting, searching, graphs, and hashing; examining performance trade-offs, analyzing runtime and memory usage.
- Algorithms: computer algorithms for numeric and non-numeric problems; design paradigms; analysis of time and space requirements of algorithms; correctness of algorithms.
- Discrete Structures for Computing: foundations from discrete mathematics for algorithm analysis, focusing on correctness and performance; introducing models like finite state machines and Turing machines.
- Mathematical Foundations: Calculus, Probability, and Linear Algebra.
Admissions
Curriculum
To qualify for this certificate, you must complete 12 semester credit hours (SCH) of coursework from the following list of courses. All courses must be completed with a grade of C or above. Each course is linked to its course description within the catalog.
Courses (12 credits):
Select four of the following:
- CSCE 625 - Artificial Intelligence
- CSCE 633 - Machine Learning
- CSCE 635 - AI Robotics
- CSCE 636 - Deep Learning
- CSCE 642 - Deep Reinforcement Learning
* Additional courses are available with the consultation of an academic advisor.