Artificial Intelligence and Data Science (AIDA)

AIDA 100 Intro to AI 3.0 SH <SLP> [GESN] 

Coverage of modern artificial intelligence, including history, present state and possible future of AI; practical use of AI in creative and intellectual work; social, philosophical and ethical issues in AI; and mathematics and algorithms of AI.
Prerequisite: Success in this course depends upon students having successfully completed the equivalent of two years of high school algebra and one year of high school geometry with grades of C or above.

AIDA 342 Machine Learning I 4.0 SH

Mathematics and software design of machine learning systems, including analytical and generative applications. Deep learning, including CNNs, transformers, VAEs, and reinforcement learning. Non deep-learning algorithms such as support vector machines and decision trees. Students will build and train a machine learning system.
Prerequisite: C or better in CSCI 171, CSCI 182, CSCI 220, and CSCI 240. MATH 273 and MATH 366 are also required.
Class Level Restriction: Sophomore and Junior and Senior only.

AIDA 344 Data Science I 4.0 SH

Theoretical and practical aspects of the analysis and display of very large datasets, including data types and data cleaning, classic statistical methods like correlation, unsupervised learning methods like K-means and other clustering, itemset analysis, sequence analysis, Bayesian analysis and other methods. Students will write code for analysis and display.
Prerequisite: C or better in CSCI 171, CSCI 182, CSCI 220, and CSCI 240. MATH 273 and MATH 366 also required.
Class Level Restriction: Junior and Senior only.

AIDA 352 Machine Learning II 3.0 SH

Advanced theoretical topics in AI, including deep learning initialization and activation functions, theoretical analysis of deep learning training, alternative function approximators such as KANs, NeRFs and 3-D learning, and a deep look at reinforcement learning.
Prerequisite: AIDA 342.

AIDA 354 Data Science II 3.0 SH

Advanced algorithms and methods in Data Science, including graph and network analysis, causal calculus, and dimensional reduction.
Prerequisite: AIDA 344.

AIDA 382 AI & Data Science Practicum 4.0 SH

Application of Data Science and AI to the solution of practical problems, with emphasis on software engineering and project management. Students will identify a currently unsolved problem in data analysis or pattern recognition of interest to a customer and will implement a solution using appropriate methods and algorithms.
Prerequisite: AIDA 342 and AIDA 344.