RM 742
Data Sci via Machine Learning · 4 credits · Spring 2027
Requirements
Prerequisites: MATH 241, MATH 231, CSCI 111 (or equivalent)
About this course
Philosophy of modeling and learning using data. Prediction using linear, polynomial, interaction regressions and machine learning including neural nets and random forests. Probability estimation with asymmetric cost classification. Underfitting vs. overfitting and R-squared. Model validation. Correlation vs. causation. Interpretations of linear model coefficients. Formal instruction of statistical computing. Data manipulation and visualization using modern libraries. Writing Intensive. Recommended corequisites include ECON 382, MATH 341, MATH 369 or their equivalents.
Seat status as of Oct 2, 8:21 PM ET. Seats can change between refreshes.