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ARIN100 Fundamentals of Artificial Intelligence (3 semester hours)
This introductory course is designed for first-year undergraduates with no prerequisites. Students will explore the fundamentals of artificial intelligence through its core concepts and applications of AI, with emphasis on quantitative reasoning and logical analysis. Interactive lectures, hands-on assignments, and collaborative discussions provide learners with both knowledge and practical skills. Key AI concepts include machine learning, neural networks, deep learning, and natural language processing, along with the role of AI in business, important ethical considerations, and its broader impact on society. Students gain applied experience using industry-standard tools and models and build a strong understanding of how AI systems work while preparing them for future study in this dynamic field.
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN102 Prompt Engineering (3 semester hours)
This course introduces students to prompt engineering, the art of designing and refining effective inputs for generative AI and large language models. Students explore prompt engineering techniques such as zero shot prompting, few shot prompting, chain of thought prompting, and tree of thought prompting to support complex tasks and improve desired output quality.
The course also examines how user prompts, precise instructions, and additional context influence AI model behavior and model’s responses across different prompt engineering use cases. Students work with AI tools and generative AI models to practice optimizing prompts that produce accurate responses, relevant output, and clearly defined desired outcomes.
Hands-on exercises and projects provide practical experience in crafting effective prompts, applying prompt engineering best practices, and analyzing intermediate steps in complex reasoning. By course end, students develop core prompt engineering skills and critical thinking abilities needed to use artificial intelligence systems responsibly and effectively across diverse applications. (Prerequisite: ARIN100)
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN202 AI Software Foundations (3 semester hours)
This software principles course introduces core concepts of software development, focusing on computer programs, software systems, and the relationship between application software, system software, and the operating system within a computer system. Students learn how programming languages, source code, machine code, and algorithms are used to create a software program that performs specific tasks on modern computer hardware.
The course covers essential topics such as data structures, software design, and software quality, while emphasizing how developers write, test, and implement code to meet user requirements. Students also examine software licenses, intellectual property rights, and common software development practices used across different types of software, including cloud-based software systems and platform-level services.
By building a strong foundation in computer science principles, students develop the understanding needed to design efficient programs and support reliable software functionality, preparing programmers and future developers for advanced applications in AI and modern technology environments.
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN210 Machine Learning in Business (3 semester hours)
This course provides an introduction to machine learning concepts and techniques with a focus on their application in business environments. Students will learn how to apply machine learning algorithms to real-world business problems, analyze data effectively, and interpret model outcomes to support strategic decision-making.
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN211 User Experience Design (3 semester hours)
This course explores the principles and practices of UX design within the context of artificial intelligence (AI) applications. Students will learn how to design intuitive, accessible, and ethical AI-powered products that enhance user interaction with intelligent systems. The course emphasizes user-centered UX design process methodologies tailored to AI systems, including chatbots, recommendation systems, and autonomous interfaces. Topics include user research, usability and user testing, ethical considerations, and strategies for integrating AI tools into the design workflow while maintaining human oversight. By combining UX research and design principles with practical applications of relevant AI tools, students will develop the ability to balance human creativity with technology to build trust, ensure inclusivity, and design AI for UX responsibly.
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN220 Artificial Intelligence and Software Development (3 semester hours)
This AI coding course explores how artificial intelligence and generative AI tools can be used to enhance coding and software development productivity. Students learn to leverage AI tools, including large language models, to support computer programming tasks such as generate code, debugging, and testing, strengthening core coding skills and applied problem solving skills.
The course covers AI concepts and practical AI solutions such as AI-powered code assistants, automated bug detection to fix bugs and code errors, and conversational AI workflows. Emphasis is placed on hands on projects and hands on experience that demonstrate real world applications of AI and machine learning in modern development environments.
Through applied exercises and hands on practice, students gain a foundational understanding of how machine learning, natural language processing, and responsible artificial intelligence technology can be integrated into development workflows. The course prepares aspiring AI engineer and software development professionals to leverage AI effectively while considering ethical considerations and responsible AI use in practical AI applications.
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN305 Artificial Intelligence Models & Data Resources (3 semester hours)
This AI evals course covers the role of data in refining and evaluating AI systems, emphasizing AI evals, evaluation metrics, and evaluation techniques used to assess generative AI and modern AI applications. Students explore how evaluation criteria, objective criteria, and clear metrics are used to measure AI outputs and model performance, particularly in complex AI systems that differ from traditional software testing.
The course examines evaluating AI systems through a systematic approach, including llm evaluation, evaluating AI agents, and the use of representative data and ground truth. Topics include error analysis, identifying failure modes, handling edge cases, and understanding how evaluation supports continuous improvement and user trust.
Through hands-on activities and real world examples, students develop practical skills to implement effective AI evals and support continuous evaluation of evolving AI products. The course prepares data scientists and technical practitioners to apply AI evaluation methods aligned with business requirements and reliable system performance. (Prerequisites: ARIN102 and CSCI360)
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN350 Artificial Intelligence Applications (3 semester hours)
This AI app development course covers the architecture and development of AI applications. Students explore components such as the underlying large language models, the AI model, orchestration layers, vector databases, and the user interface layer that together form an AI system. Hands-on activities include implementing orchestration frameworks, using prompt templates, and applying prompt engineering techniques to support effective AI integration.
By synthesizing prior knowledge with practical techniques, students gain the skills to developing AI applications and build a functional app for real world scenarios. The course prepares AI developers and software developers to build AI powered applications using modern AI tools, software development practices, and artificial intelligence capabilities. (Prerequisite: ARIN305)
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN360 Collaborative Tools (3 semester hours)
This course provides students with practical skills to streamline teamwork in AI development through a beginner friendly introduction to version control, version control systems, and git version control. Students learn git and github as essential tools for managing a repository, creating branches, tracking changes, and collaborating effectively on a shared repo in a central place. Through hands-on examples and guided projects, learners practice making commits, reviewing work, and learning how to resolve conflicts that arise during collaborating on coding tasks. The course also introduces best practices for managing files, addressing bugs, and contributing to open-source communities. By the end of the course, students understand core basics of collaborative development and build the ability to enhance productivity and teamwork in professional AI and software development environments. (Prerequisite: ARIN305)
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN410 Artificial Intelligence Impacts, Risks & Ethics (3 semester hours)
This AI ethics course examines the societal and economic impacts of artificial intelligence, focusing on the ethical challenges, ethical implications, and ethical issues associated with modern AI systems and artificial intelligence technologies. Students explore AI risks such as data privacy, security risks, AI biases, and unfair outcomes, alongside workforce disruption and social implications. The course introduces key ethical frameworks, ethical considerations, and ethical practices used to support responsible AI, trustworthy AI, and effective AI governance across the AI lifecycle. Topics include machine learning, generative AI, training data, data governance, AI regulation, and the role of government regulation and the private sector in managing AI development. Through applied discussions and case studies, students learn to address issues, evaluate potential risks, and promote AI ethics that respect human rights while supporting innovation and sound decision making. (Prerequisite: ARIN100)
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN450 Artificial Intelligence Advance Topics (3 semester hours)
This course explores advanced topics in artificial intelligence, focusing on advanced topics shaping the future of AI systems and artificial intelligence systems. Students examine scaling laws, computational power, and the role of inference-time compute in large language models, deep learning, and neural networks. Additional topics in artificial intelligence include reinforcement learning, edge computing, large datasets, expanding context windows, and emerging natural language processing capabilities. Through theoretical analysis and applied exploration, students gain knowledge of state of the art AI techniques, machine learning algorithms, and advanced applications across various industries and business applications. This advanced topics in artificial course equips learners with the tools, techniques, and insight needed to stay ahead of rapidly evolving emerging technologies in artificial intelligence. (Prerequisite: ARIN350)
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.
ARIN499 Artificial Intelligence Capstone (3 semester hours)
This artificial intelligence capstone course provides a culminating capstone experience in which students apply foundational concepts, practical skills, and technical expertise developed throughout the program. In this capstone project, learners develop, create, and evaluate a comprehensive AI solution addressing real world challenges and real world applications. Projects may incorporate machine learning, deep learning, natural language processing, computer vision, and modern ai tools, with emphasis on hands on experience, deployment considerations, and professional communication. Students work as a team to manage a complete project lifecycle, demonstrating ability, analysis, and evaluation skills. The course concludes with a formal presentation and assessment, allowing learners to showcase a professional portfolio that supports career readiness and reflects applied artificial intelligence knowledge.
View the course schedule AMU or APU to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.