Basic concepts needed to write computer programs. Simple program design and implementation using a specific programming language; ((P) Python. Each alpha repeatable unlimited times, but credit earned one time only. ((P) Cross-listed as ICS 110P)
Introduction to contemporary mathematical methods for empirical inference, data modeling, and machine learning. A-F only. Pre: MATH 241, MATH 203, MATH 215, or MATH 251A. (Fall only) (Cross-listed as ICS 235)
Computational and statistical methods for analyzing network models of social, technological, information, and biological networks. Introduction to relevant data analytics and graph analysis software packages. Pre: (ICS 311 or ECE 367) or consent. (Cross-listed as ICS 422)
Introduction to critical statistical and probabilistic concepts that underlie data science as well as tools that play a central role in the daily work of a data scientist. A-F only. Pre: 211 or consent. (Cross-listed as ICS 434)
Introduction to machine learning concepts with a focus on relevant ideas from computational neuroscience. Information processing and learning in the nervous system. Neural networks. Supervised and unsupervised learning. Basics of statistical learning theory. Pre: 235, or consent. Recommended: MATH 307. (Once a year) (Cross-listed as ICS 435)
Introduction to data visualization through practical techniques for turning data into images to produce insight. Topics include: information visualization, geospatial visualization, scientific visualization, social network visualization, and medical visualization. Junior standing or higher. Pre: CINE 215 or ICS 110(Alpha) or ICS 111. (Cross-listed as CINE 484 and ICS 484)