Artificial Intelligence and Machine Learning

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The Associate in Applied Science (AAS) in Artificial Intelligence and Machine Learning focuses on building machine learning models that can be used for predicting, making decisions and enhancing human capabilities. The degree provides students with the essential knowledge and skills in Artificial Intelligence and Machine Learning technologies and their application in business and industry. Students will sharpen their skills in prompt engineering, study machine learning models, natural language processing and computer vision algorithms, and gain hands-on experience with popular programming languages, tools and platforms used in AI development. The curriculum also includes coursework in programming, computer security, math, Database and statistics

Program Purpose

Artificial Intelligence (AI) is no longer a niche technology—it is a foundational component of modern business, healthcare, manufacturing, cybersecurity, and countless other sectors. Employers increasingly seek professionals with practical AI skills to design, implement, and maintain intelligent systems. However, there is a significant gap between industry demand and the availability of workforce-ready graduates with applied AI expertise.

Learning Outcomes

• Demonstrate Understanding of AI and ML Fundamentals :

Explain core concepts, terminology, and principles of Artificial Intelligence and Machine Learning.
 

• Apply Machine Learning Algorithms to Real-World Problems:

Implement supervised, unsupervised, and reinforcement learning techniques using industry-standard tools.

•  Develop and Deploy AI Solutions:

Design, train, and evaluate AI models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.

• Perform Data Preparation and Feature Engineering

Collect, clean, and preprocess data for effective AI and ML model development.

• Integrate AI into Business and Technical Applications

Apply AI and ML solutions to domains such as automation, predictive analytics, and decision support systems.
 

• Address Ethical, Legal, and Social Implications of AI