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.

Each student or small group will develop a project, be matched with a Global Studies faculty mentor, identify relevant community groups, and spend the semester working on that project. Students will discuss ideas, formulate plans, identify tactics, and engage with important social justice literatures. Importantly, the course will engage with the project of activism itself, which has the potential to replicate systems of inequality.

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.

What are the strengths and weaknesses of the major policy-making institutions, and how does the current system of American governance compare with that of other advanced societies? This class will examine the key institutional and political actors in policymaking; focusing on the increasing fole of non-governmental institutions in problem solving.

In this course, students gain experience applying global perspectives, as well as research methods and techniques, to one of several real-world issues. Team-taught, the course allows students to choose a path that includes a methodological foundation, a deep dive into a particular method, a chance to practice a useful skills related to Global Studies professions, and culminating in the applied research project.

This course examines how the world's major states and regions manage their public finances and economic policies through their budgetary processes and institutions.

LPPL 4225 is designed to foster three critical skillsets:  1) The expansion of your self-awareness to enhance your competence as a leader, 2) Learning ways to support and inspire the development of strengths in others, and 3) Combining these skills to improve the effectiveness of your student organizations at UVA by reflecting on the organizational and interpersonal dynamics of those groups.