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AI Skills Building

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Training for the Future

At Case Western Reserve University, we are committed to preparing our campus community for an AI-driven future. Our diverse educational pathways in AI are designed to equip students, faculty, alumni, and lifelong learners with the skills they need to thrive in today’s and tomorrow’s workforce. Whether aiming to drive AI innovation, explore its interdisciplinary applications, or build foundational literacy, our programs support all levels and interests.

Empowering Our Campus Community and Beyond with Generative AI Education

Learning Pathways for All

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We provide flexible AI learning pathways for all levels, from beginners to advanced learners. Individuals can choose from a range of options, including introductory courses, specialized degrees, research, and both internal and external training modules. Regardless of the path, our culture of innovation equips the campus community with the skills to adapt and thrive in a technology-driven world.

AI Course Offerings

With more than 100 courses spanning 40 departments that integrate AI, the featured courses below provide diverse opportunities to gain a comprehensive understanding of AI. Including its development, application, and ethical implications across various disciplines. Detailed course registration information is available in SIS.

  • BUAI 446 - Machine Learning and Artificial Intelligence in Business Analytics
  • Advances in computational analytics including Machine, Deep and Statistical Learning (ML) provide powerful methods for developing mathematical "learning" models that can autonomously parse, learn from, and make predictions from data to improve performance with "experience". In deep learning, large neural networks are leveraged to achieve AI, enabling machines to mimic human behavior. This course covers principles, algorithms, and applications of machine learning from a business analytics perspective.
  • COGS 250 - Responsible AI: Cultivating a Just and Sustainable Socio-technical Future through Data Citizenship
  • An introduction to the key issues that inform ethically responsible design, deployment, and use of AI technologies, with particular focus on the impact of data practices. In this praxis-oriented course, we will explore how data is fundamental to the development of AI technologies and develop practices for increased awareness of and participation in this data ecosystem. As we interrogate AI systems in everyday life through hands-on engagement with AI tools and their data pipelines, we will begin to construct a data citizenship model that can help us reclaim the power of collective responsibility in order to build a more just and sustainable socio-technical future.
  • CSDS 340 - Introduction to Machine Learning
  • Machine learning is a sub-field of AI that is concerned with the design and analysis of algorithms that "learn" and improve with experience, While the broad aim behind research in this area is to build systems that can simulate or even improve on certain aspects of human intelligence, algorithms developed in this area have become very useful in analyzing and predicting the behavior of complex systems. This course is an introduction to algorithms for machine learning and their implementation in the context of big data.
  • ENGR 420C - Artificial Intelligence: Sequential Decision Making
  • This course introduces advanced AI models in the areas of computer vision and natural language processing as well as reinforcement learning techniques along with their implementation for industrial applications. The primary focus is on deep learning-based modeling with a brief introduction to traditional computer vision and NLP techniques.
  • EBME 463 - Biomedical Signal Processing and Machine Learning
  • AI in medical imaging is experiencing tremendous growth all over the world. Biomedical imaging and its analysis are fundamental to understanding, visualizing, and quantifying medical images in clinical applications. With the help of automated and quantitative image analysis techniques, disease diagnosis will be easier/faster, and more accurate, leading to significant development in medicine in general. This course aims to help students develop skills in artificial intelligence and machine learning techniques applied to biomedical image analysis.
  • PHIL 393 - Ethics of Artificial Intelligence and Emerging Technology
  • This course will explore ethical issues concerning the development and use of Artificial Intelligence and related emerging technologies, ranging from deferring to automation as an authority to the effects of bias embedded in algorithms to vital questions of autonomy and accountability, all the way to an examination of possible rights for advanced AI systems in the future.
  • PQHS 416 - AI in Medicine: Knowledge Representation and Deep Learning
This course introduces students to computational techniques and concepts that underpin biomedical and health informatics data management and analysis. In particular, the course will focus on the three topics of: (1) Biomedical terminologies and formal logic used in building knowledge models such as ontologies; (2) Natural language processing (NLP), and (3) Big Data technologies, including components of Hadoop stack and Apache Spark.

Undergraduate Degrees in AI

  • Minor in Artificial Intelligence – Offered through Computer Science and Data Sciences, this minor provides foundational AI skills.
  • NEW Major in Artificial Intelligence (In Development) – Offered through Computer Science and Data Sciences, this major provides advanced AI skills.
  • NEW Major in Humanity and Technology (In Approval) – Offered through the College of Arts and Sciences, this interdisciplinary major brings together humanities and AI technology.

Specialized AI Programs

  • Getting Started with Generative AI​ in Education - this self-paced course, offers a concise foundation in Gen AI and takes about 3-4 hours to complete. This course was developed in response to the CWRU AI Task Force report released by the Faculty Senate and Office of the Provost, is a collaborative effort by [U]Tech TLT, Kelvin Smith Library, the University Center for Innovation in Teaching and Education, and faculty members. Learn more and enroll in the course.
  • Teaching and Learning with Generative Artificial Intelligence Systems - this course provides guidance for integrating Gen AI into teaching and learning and it highlights strategies to incorporate Gen AI tools in the classroom, ensuring they align with pedagogical goals and foster student engagement. The resource also covers the ethical considerations of using AI.