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DATA 235 Machine Learning Methods (3)

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)

DATA 434 Data Science Fundamentals (3)

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)

DATA 435 Machine Learning Fundamentals (3)

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)

DATA 484 Data Visualization (3)

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: ACM 215 or ICS 110(Alpha) or ICS 111. (Cross-listed as ACM 484 and ICS 484)