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IU Indianapolis Bulletin » Schools » Luddy School of Informatics, Computing, and Engineering » Courses » Artificial Intelligence Courses

Artificial Intelligence Courses

  • AII-I 100 Introduction to Artificial Intelligence (3 cr.) This course presents current real-world applications of AI with multiple case studies. Students learn the history of AI and how its coming pervasiveness could impact the future. Topics include heuristic search, machine learning, automated decision-making, and interaction with the physical world. Programming-based assignments enable students to learn AI techniques.
  • AII-I 200 Introduction to Data Science (3 cr.) This course introduces data science and programming in the R statistical computing environment. Students learn relevant concepts from statistics, mathematics, and computer science. Topics include data manipulation, analysis, modeling, and visualization. Students gain experience analyzing real-world datasets from science, government, and industry.
  • AII-I 300 Collaborative Human–AI Systems (3 cr.) This course introduces human-AI interaction design for systems that solve problems neither humans nor artificial intelligence could solve alone.Topics include interpretability, transparency, trust, and AI ethics. Student projects focus on developing applications where AI provides cognitive and perceptual augmentation to humans.
  • INFO-B 443 Natural Language Processing (3 cr.) P: INFO-B 210 OR CSCI-A 204 OR CSCI-C 200 OR CSCI 23000; Recommended: Statistics (ECON-E 270 or PBHL-B 280 or PBHL-B 300 or PBHL-B 301 or PBHL-B 302 or PSY-B 305 or SPEA-K 300 or STAT-I301 or STAT-I350) OR INFO-I 415 This course introduces the theory and methodology of natural language understanding and generation. Topics include stemming, lemmatization, parts of speech tagging, parsing, and machine translation. Employing specialized libraries, students develop applications for topic modeling, sentiment analysis, and text summarization.
  • INFO-B 585 Biomedical Analytics (3 cr.) Course introduces the use of patient data, genomic databases, and electronic health records (EHR) to improve patient care and to achieve greater efficiencies in public and private healthcare systems. The course explores clinical intelligence and the role of analytics in supporting a data-driven learning healthcare system. Topics include the value-driven healthcare system, measuring health system performance, existing quality/performance measurement frameworks (NQF, HEDIS), comparing healthcare delivery, attributes of high performing healthcare systems, and the IT infrastructure and human capital needed to leverage analytics for health improvement.
  • INFO-B 621 Computational Techniques in Comparative Genomics (3 cr.) Course will summarize computational techniques for comparing genomes on the DNA and protein sequence levels. Topics include state-of-the-art computational techniques and their applications: understanding of hereditary diseases and cancer, genetic mobile elements, genome rearrangements, genome evolution, and the identification of potential drug targets in microbial genomes.
  • CSCI-B 355 Autonomous Robotics (3 cr.) Introduction to the design, construction, and control of autonomous mobile robots. This course covers the basic mechanics, electronics, and programming for robotics, and the applications of robots in cognitive science.
  • CSCI-B 551 Elements of Artificial Intelligence (3 cr.) Introduction to major issues and approaches in artificial intelligence. Principles of reactive, goal-based, and utility-based agents. Problem-solving and search. Knowledge representation and design of representational vocabularies. Inference and theorem proving, reasoning under uncertainty, and planning. Overview of machine learning.
  • CSCI-P 558 Deep Learning (3 cr.) P: CSCI-B 551 Elements of Artificial Intelligence or CSCI-B 555 Machine Learning or CSCI-B 565 Data Mining or INFO-H 515 Statistical Learning This course covers deep learning neural networks. Topics include logistic regression, feedforward networks, autoencoders, convolutional neural networks, recurrent neural networks, graph neural networks, deep generative models, adversarial and reinforcement learning, and optimization and regularization techniques. Students also delve into recent research and learn through projects to develop deep learning systems.
  • INFO-I 219 Software Bots for Cognitive Automation (3 cr.) This course introduces the development of software bots for process and cognitive automation. Students learn how organizations adopt artificial intelligence and related technologies to process unstructured and uncurated data in various industries. The course also examines the disruptive effects of process and cognitive automation on social, economic, and global environments.
  • INFO-I 220 The Social Impact of Bots and Automation (3 cr.) This course examines the disruptive effects of process automation on social, economic, and global environments and how organizations adopt artificial intelligence and other technologies to process unstructured and uncurated data. The course also introduces applications of cognitive automation with bots in various industries and their implications.
  • INFO-I 319 Cognitive Automation and Bots Development (3 cr.) P: INFO-I 220 This course covers how to develop robotic process automation and cognitive automation for various kinds of organizations. Students apply artificial intelligence and bot platforms and frameworks to automate organizational processes from end to end.
  • INFO-I 340 Collaborative Human–AI Systems (3 cr.) This course introduces human–AI interaction design for systems that solve problems neither humans nor artificial intelligence could solve separately.Topics include interpretability, transparency, trust, and AI ethics. Student projects focus on developing applications where AI provides cognitive and perceptual augmentation to humans.
  • INFO-I 419 Enterprise Cognitive Automation (3 cr.) P: INFO-I220 This course covers the integration of cognitive automation in business process management systems. Students model organizational processes and integrate artificial intelligence (AI) to increase and monitor their efficiency and effectiveness.They also learn from cognitive automation use cases how enterprises manage processes across systems, applications, and data repositories.
  • INFO-I 428 Web Mining (1-3 cr.) P: INFO-B 210 or CSCI-A 204 or CSCI 23000 or CSCI-C 200 This course covers concepts and methods used to search the web and other sources of unstructured text from a human-centered standpoint. These include document indexing, crawling, classification, and clustering; distance metrics; analyzing streaming data, such as social media; link analysis; and system evaluation.
  • INFO-I 482 Conversational User Interfaces: Experience Design and Applications (3 cr.) This course introduces the fundamentals of user experience design for conversational computing. Students explore the cognitive, experiential, and social aspects of conversational user interaction through applied projects, labs, and discussions. Students also learn tools and methods for designing, prototyping, and testing conversational user experiences.
  • INFO-I 496 Artificial Intelligence Professional Practice 1 (3 cr.) This course covers the development of a project proposal in artificial intelligence to meet business requirements using system analysis and design methods. Students identify a business problem that can be solved with AI, either independently or with an industrial partner; research the solution; and develop a plan for solving it.
  • INFO-I 497 Artificial Intelligence Professional Practice 2 (3 cr.) This course covers the implementation of a project in artificial intelligence to meet business requirements using system analysis and design methods. Students develop and deploy an AI solution to a business problem based on the plans and designs in their project proposal.