Computer Science

This course focuses on concepts in data structures and algorithms, the foundational skills necessary for developing a career in computer science and related fields. Topics covered include abstract data types, static and dynamically allocated data types (arrays, linked lists, stacks, queues), hierarchical data types (trees, graphs), hashing schemes, theory of algorithms, correctness theorem, mathematical induction, and recursion. Additional topics include divide-and-conquer, dynamic programming, backtracking, and randomized algorithms.

This course focuses on concepts in Object-oriented programming and software design patterns using the C++ programming language, towards the goal of designing and developing a complex computing solution. Topics include basic language syntax, file I/O, function pointers, complex data types, and low-level programming. Additional topics include C++-specific modules on Object-oriented software design – classes and objects, single and multiple inheritance, polymorphism, and overloading, as well as software- and games-based design patterns and anti-patterns.

This course covers exploratory data analysis using the Python programming language. Topics covered include Python syntax, object-oriented programming in Python, data frames and array manipulation using NumPy and Pandas/Polars, basic concepts of data processing, cleaning, and summarization. Additional topics may include data visualization using Matplotlib, and practical AI modeling using scikit-learn.

This course supports mixed-discipline game, game-adjacent or Computer Science projects in preproduction, production, or post-production with a focus on the application of discipline-based skills. Project and pipeline management techniques will be applied, including team dynamics, cross-discipline integration, and best practices of the product development cycle in game production.

This course covers game design principles and the theory behind what makes games engaging. Topics include design process, playtesting, pacing, and overall arc structure.

This course covers the techniques used to build dynamic user interfaces for games, how to automate and debug gameplay, model and control physics-based movement, and building complex multi-part game entities.

This course focuses on analysis and simulation of game systems from a technical perspective. The focus is on both economic and combat systems, with automation and telemetry used for debugging, analyzing, and balancing those systems.

This course focuses on methods for creating engaging spatial environments, using procedural generation and traditional level editors. Additional topics include encounters to control progression, player guidance through space, and telemetry for level analysis.

This course focuses on the elements of user experience design, along with technical techniques that enable dynamic user interfaces and the ability to test them effectively. Also covered are methods for creating feedback in a variety of modalities (visual, audio, etc.) that reinforce each other and create a structured arc of feedback over time.

This course covers current trends in game design and development. Topics covered include Design, technical, audio, artistic, production, and business trends.

This course covers concepts in data analysis using Artificial Intelligence (AI). Topics include key principles of learning theory, methods for model selection and evaluation, regression analysis, and classification algorithms. Additional topics include probability and statistics as they are applied in data analysis.

This course focusses on the study, field, and practical application of data visualization. Topics covered include techniques for creating effective static and interactive visualizations using graphic design, perception, psychology, and cognitive science. Additional topics may include emphasis on critiquing and creating interactive visualizations of complex systems, such as stock market trading systems and large software projects (static code, software architecture, software evolution, software execution, and data flow).

This course focuses on the theory and applications of neural networks and deep learning. Topics include artificial neural networks, backpropagation, hyperparameter selection and optimization methods in deep learning, convolutional and recurrent neural networks, deep reinforcement learning, and a discussion of generative models. Additional topics may include topical advancements in deep learning.

This course focuses on data analysis using Artificial Intelligence (AI). Topics include clustering, data reduction, interpretable Machine-Learning algorithms such as decision trees and random forests, reinforcement learning and recommender systems. The course also reinforces the data science life cycle through projects.

This course provides a comprehensive exploration of Large Language Models (LLMs), covering their theoretical foundations, architectural innovations, training methodologies, and real-world applications. Topics covered include understanding of transformer architectures, attention mechanisms, and the scaling laws that govern modern language models. The course emphasizes both theoretical principles and practical implementation, preparing students to contribute to cutting-edge research and industrial applications in natural language processing.

This course examines the technical, economic, and societal forces reshaping artificial intelligence today, with a focus on agentic AI systems, the global AI race, and the governance challenges facing industry and policymakers alike. Topics include the rise of autonomous AI agents and their enterprise adoption challenges, global competition in AI development, ethical concerns driven by AI-powered automation, and the sustainability crisis posed by AI's growing energy demands. Additional topics may include the regulatory landscape for AI tools, open-source AI and its geopolitical implications, AI safety and security vulnerabilities such as prompt injection, and organizational strategies for responsible AI deployment.