Introduces foundational knowledge and emerging trends in health informatics, and examines how information systems can be utilized to improve patient care, health outcomes, efficiency, and quality. Provides knowledge on how health informatics can enhance evidence-based decision making, cost-management, and performance; analyzes key issues in data management, and confidentiality in health informatics. Prerequisite: Admission to BPHM or BIS Program.

This course is focused on an exploration of "self" in relationship to the complexities and structures of the professional organizations in which students work as interns. The course combines organizational behavior concepts and content that emphasizes self and exploration.

The course is focused on an exploring the dynamics of teams and leadership within the complexities and structures of the organizations in which students work in professional practice internships. The course combines organizational behavior with concepts of teams and organizations.

Students apply academic experiences in professional and/or research settings; reflect and critically and constructively analyze experiences from multiple perspectives; and view the work as connecting course content authentic contexts. Students work as professionals with site supervisors and instructors to complete related assignments and relevant background research on the professional and academic resources available.

An Independent Study in Archaeology. Subject to be determined by student and instructor.

Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.

Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic dashboards for effective data communication with end-users. Covers theoretical concepts and hands-on implementation to provide a comprehensive understanding of the full design process.

Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine learning methods. Covers definitions, decision support, trust, and ethical considerations, and the latest advances in creating reliable and transparent AI models.

Explores the tools and techniques used to assess the security posture of a target system. Topics include footprinting, reconnaissance, vulnerability discovery, and malware. Covers vulnerability discovery in a variety of systems, including web applications, mobile platforms, and cloud computing. Aligns with the EC-Council ANSI accredited Certified Ethical Hacker exam 312-50.

Understand Deep Learning covering neural networks, activation functions, and optimization algorithms. Gain experience with TensorFlow and PyTorch, mastering key techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Explore transfer learning, reinforcement learning, and natural language processing (NLP), along with industry applications and ethical considerations.