6 courses with the subject AI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
AI 310Introduction to Artificial Intelligence3
This course delves into the core principles and methods of artificial intelligence. Key areas of focus include solving problems using search algorithms, representing knowledge, reasoning with probabilities, the basics of machine learning, and the ethical dimensions of AI. Through practical projects, students will acquire hands-on skills in creating, executing, and utilizing AI systems to address real-world challenges.
This course provides an introduction to the fundamental concepts and techniques of machine learning. Primary topics include supervised and unsupervised learning, model development, and performance evaluation. Through hands-on projects and practical examples, students will explore how machine learning techniques are applied to solve real-world problems. The course emphasizes building a strong conceptual framework and practical skills for further study in artificial intelligence.
This course provides an in-depth exploration of advanced concepts and techniques in Artificial Intelligence. Primary topics include advanced search frameworks, planning, complex decision-making, multi-agent systems, advanced deep learning models, and modern AI-based agentic systems. Students will investigate state-of-the-art algorithms, architectures, and models that power intelligent systems, while gaining practical experience through hands-on projects that emphasize the design, implementation, and evaluation of AI solutions. The course also covers emerging trends in AI research and real-world applications across diverse domains, fostering critical thinking about the opportunities and challenges of building intelligent technologies with modern tools and frameworks.
This course provides an in-depth exploration of advanced machine learning techniques, building on foundational concepts and practical skills in machine learning. This course emphasizes theoretical underpinnings in machine learning models and algorithms, practical implementation, and real-world applications of cutting-edge methods. Primary topics include supervised and unsupervised learning, deep learning, probabilistic graphical models, and optimization techniques in learning problems. Hands-on projects will involve implementing models from scratch as well as using modern frameworks and analyzing performance and behaviors from both theoretical and empirical perspectives. The course also covers emerging trends in machine learning, such as interpretability, fairness, and robustness, preparing students for research or industry roles in this rapidly evolving field. By the end, students will be equipped to design, evaluate, and deploy sophisticated machine learning systems.
