Introduction of Python programming for management of textual, tabular, and image data, used in conjunction with JSON, NumPy, Pandas, and LangChain for data management, cleaning, wrangling, aggregation, and interfacing. Credit cannot be given for both IAI 110/ASE110 and CIS 151.

Introductory concepts in set theory, probability theory, linear algebra, and the fundamentals of how they are applied to artificial intelligence. IAI302/ASE302 cannot be taken for credit after receiving a grade of C or higher in MAT331, MAT485, or CIS375.

Foundational concepts in artificial intelligence, including agents, knowledge representation and reasoning, machine learning, neural networks, reinforcement learning, and large language models, accompanied by programming projects and exercises.

Introduction to AI-powered tools for programming, including the use of language models (LLMs) for code generation and testing. Development of simple LLM-based applications, including Retrieval-Augmented Generation and basic AI agents.

Artificial Intelligence System Specification and Design; AI software libraries and tools; Project Management; Artificial Intelligence System Architecture; Incremental Product Delivery; Prototyping; Evaluation; Team projects. Additional work required for graduate students.

Artificial Intelligence System Development; Incremental Product Delivery; Evaluation; Maintenance; Team projects; Additional work required for graduate students.

Computational and hardware foundations of ML; complexity and performance analysis of ML models; modern computer architectures; FPGA-based acceleration; model optimization techniques and their impacts. Additional work required for graduate students.

Algorithmic and mathematical foundations of the Knowledge Discovery in Databases (KDD) process, including data representation, preprocessing, supervised and unsupervised learning, and recommendation systems, with emphasis on computational efficiency, scalability on massive-scale data, and performance evaluation using modern analytical tools.

Feature extraction; Supervised and unsupervised learning; Bias-variance trade-off; Linear, logistic, and nonlinear regression; Decision trees and ensemble models; Neural networks; Deep Learning. Additional work required for graduate students.

Core artificial intelligence algorithms, including tree search and constraint satisfaction, automated reasoning, probabilistic models and Monte-Carlo methods, reinforcement learning, and automatic differentiation. Additional work is required for graduate students.

Exploration of a problem, or problems, in depth, individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.

A mix of discussion sessions and case study explorations on potential shortcomings in AI. Students will draw on a growing body of real-world experiences and knowledge related to AI regulation, privacy and security, explainability, and data integrity.

Completion of an Honors Capstone Project under the supervision of a faculty member.

The language of sets, propositional and predicate logic. Methods of proof including mathematical induction. Linear equations, matrices and Gaussian elimination. Vector spaces, linear independence, basis and dimension. Linear transformations, orthogonality, Gram-Schmidt orthogonalization. Eigenvalues, eigenvectors and diagonalization.

Foundational concepts in artificial intelligence, including knowledge representation and reasoning, machine learning, and neural networks. Introduction to the function, employment, and limitations of language models (LLMs), and proper LLM usage in coding tasks.