Academic Programs
Professional M.S. in CS (PMCS)
Industry-aligned M.S. with AI & Data Science tracks. Project-based courses, partner-led capstones, and seminar series.
Learn moreGraduate Certificate in Applied Computing
Intensive, stackable pathway covering AI foundations, data, and software for working professionals and graduates.
Learn moreCapstone Projects
Capstone & Applied Projects Program connects students with real-world AI, data science, cybersecurity, and visualization projects developed with agencies, nonprofits, businesses, researchers, and community partners across Hawaiʻi, emphasizing safe, responsible, and human-guided AI.
Learn moreAI-Related Courses at ICS
ICS 102 – Introduction to Applied AI (effective Fall26)
A first-year course that introduces students to generative AI, responsible use, and hands-on projects in applied AI.
UndergraduateICS 235 – Machine Learning Methods
Introduction to contemporary mathematical methods for empirical inference, data modeling, and machine learning.
UndergraduateICS 361 – Introduction to Artificial Intelligence Programming
Introduction to the theory of Artificial Intelligence and the practical application of AI techniques in Functional (Common LISP and/or Scheme) and Logic (Prolog) programming languages. Students gain practical experience through programming assignments and projects.
UndergraduateICS 434 – Data Science Fundamentals
Introduction to critical statistical and probabilistic concepts that underlie data science as well as tools that play a central role in the daily work of a data scientist.
UndergraduateICS 435 – Machine Learning Fundamentals
Introduction to machine learning concepts with a focus on relevant ideas from computational neuroscience. Information processing and learning in the nervous system. Neural networks. Supervised and unsupervised learning. Basics of statistical learning theory.
UndergraduateICS 438 – Big Data Analysis
Concepts, tools, and techniques for analyzing and mining massive data sets. Data cleaning and pre-processing. Data analysis and mining techniques. Big Data platforms. Big Data visualization.
UndergraduateICS 461 – Artificial Intelligence
Survey of artificial intelligence: natural language processing, vision and robotics, expert systems. Emphasis on fundamental concepts: search, planning, and problem solving, logic, knowledge representation.
UndergraduateICS 462 – Artificial Intelligence for Games
Techniques to stimulate intelligence in video games: movement, pathfinding with A* search, decision/ behavior trees, state machines, machine learning, tactics. Extend games with your own AI implementations; experience “shootout” contests for the best AI algorithm/implementation.
UndergraduateICS 469 – Cognitive Science
Introduces basic concepts, central problems, and methods from cognitive science. Identifies contributions from disciplines such as cognitive psychology, linguistics, artificial intelligence, philosophy, and neuroscience.
UndergraduateICS 483 – Computer Vision
Introductory course in computer vision. Topics include image formation, image processing and filtering, edge detection, texture analysis and synthesis, binocular stereo, segmentation, tracking, object recognition and applications.
UndergraduateICS 601 - Applied Computing Industry Seminar
Introduction to the importance of building and utilizing AI systems and an exploration of the current industry landscape.
GraduateICS 603 - Applied Computing Fundamentals
Provides necessary programming skills in Python for data science and AI, and introduces language models as programming copilots. Students also learn AI-assisted programming through validation, testing, documentation, optimization, and software design.
GraduateICS 604 - Applied Data Science
Introduces foundational principles of data science, emphasizing elementary concepts and essential tools integral to the data scientist’s role from data wrangling to foundational tools for modeling data, without requiring knowledge of linear algebra or calculus.
GraduateICS 605 - Applied Artificial Intelligence
Covers fundamentals of artificial intelligence, and especially machine learning, for students and industry professionals who aim to immediately implement AI and data science solutions in their career.
GraduateICS 606 - Intelligent Autonomous Agents
Theory, methods and practical applications of autonomous agent systems, including common applications of both software and hardware (robotic) agents. In-depth practical experience with autonomous agents through programming assignments and projects.
GraduateICS 635 – Advanced Machine Learning
Introduction to key theoretical concepts of machine learning. Practical experience with decision free methods, artificial neural networks. Bayesian belief networks and contemporary statistical methods including regression, clustering and classification.
GraduateICS 636 - Information Theory in Machine Learninge
Basics of information processing and learning in the brain; neural networks; learning algorithms based on information maximization; applications in molecular biology and bioinformatics.
GraduateICS 637 - Deep Learning with Neural Networks
Graduate course on deep learning with artificial neural networks. Provides practical techniques for modeling image, video, text, and graph data with supervised, unsupervised, and reinforcement learning approaches. Includes instruction in the latest software frameworks.
GraduateICS 639 - Deep Learning with Neural Networks
Graduate course on deep learning with artificial neural networks. Provides practical techniques for modeling image, video, text, and graph data with supervised, unsupervised, and reinforcement learning approaches. Includes instruction in the latest software frameworks.
GraduateICS 661 - Advanced Arthificial Intelligence
Current issues in artificial intelligence, including expert systems, knowledge representation, logic programming, learning, natural language processing.
GraduateICS 683 - Pattern Recognition
Nature of the problem in pattern recognition and clustering; explanation of various algorithms.
GraduateICS 684 - Advanced Data Visualization
Explores advanced techniques in data visualization by surveying visualization techniques from the past 5-10 years, and designing and developing data visualizations leveraging those techniques.
GraduateICS 685 - Virtual and Augmented Reality
Students will learn the science, engineering, art, and applications of virtual reality and augmented reality, with an emphasis on the construction of working virtual environments.
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