A course that provides an opportunity for supervised individual research and study in computer science. A topic of the course must be approved by a supervising instructor and the Computer Science Director of Undergraduate Studies. May be repeated to a maximum of six credits.

A course that provides an opportunity for supervised individual research and study in computer science. A topic of the course must be approved by a supervising instructor and the Computer Science Director of Undergraduate Studies. May be repeated to a maximum of six credits.

A course that provides an opportunity for supervised individual research and study in computer science. A topic of the course must be approved by a supervising instructor and the Computer Science Director of Undergraduate Studies. May be repeated to a maximum of six credits.

A course that provides an opportunity for supervised individual research and study in computer science. A topic of the course must be approved by a supervising instructor and the Computer Science Director of Undergraduate Studies. May be repeated to a maximum of six credits.

A course that provides an opportunity for supervised individual research and study in computer science. A topic of the course must be approved by a supervising instructor and the Computer Science Director of Undergraduate Studies. May be repeated to a maximum of six credits.

A course that provides an opportunity for supervised individual research and study in computer science. A topic of the course must be approved by a supervising instructor and the Computer Science Director of Undergraduate Studies. May be repeated to a maximum of six credits.

Study of fundamental concepts behind the design, implementation and application of database systems. Brief review of entity-relationship, hierarchial and network database models and an in-depth coverage of the relational model including relatinal algebra and calculi, relational database theory, concepts in schema design and commerical database languages.

Study of computational principles and techniques that enable software systems to improve their performance by learning from data. Focus on fundamental algorithms, mathematical models and programming techniques used in Machine Learning. Topics include: different learning settings (such as supervised, unsupervised and reinforcement learning), various learning algorithms (such as decision trees, neural networks, k-NN, boosting, SVM, k-means) and crosscutting issues of generalization, data representation, feature selection, model fitting and optimization.

The course covers basic techniques of artificial intelligence. The topics in this course are: search and game-playing, logic systems and automated reasoning, knowledge representation, intelligent agents, planning, reasoning under uncertainty, and declarative programming languages. The course covers both theory and practice, including programming assignments that utilize concepts covered in lectures.

This course provides an introduction to generative artificial intelligence. This course will provide students with an understanding of how to formulate generative problems, utilize generative artificial intelligence tools to create solutions, and evaluate said solutions. This course will also introduce and discuss the ethical concerns associated with generative artificial intelligence (fairness, bias, trust, explainability). This course will emphasize using generative tools (hyperparameter tuning, relationship of inputs to outputs, etc.) over programming.