MATH 342W
Data Sci via Machine Learning · 4 credits · Spring 2027
Requirements
Prereq.: ENGL 110, MATH 231, MATH 241, CSCI 111 (or equivalent)
About this course
Philosophy of modeling with data. Prediction via linear models and machine learning including support vector machines and random forests. Probability estimation and asymmetric costs. Underfitting vs. overfitting and model validation. Formal instruction of data manipulation, visualization and statistical computing in a modern language. Not open to students who are taking or who have received credit for MATH 642. Writing Intensive (W). Recommended corequisites include ECON 382, 387, MATH 341, MATH 343 or their equivalents.
Seat status as of Oct 2, 8:21 PM ET. Seats can change between refreshes.
01-LEC Regular
Mon/Wed · TBA
Kiely Hall 324
Elliot Gangaram / Bryan NevarezNo Rate My Professors rating foundIn PersonOpen