Select one of the options below:
Machine learning algorithms and tools for predictive modeling presented using case studies that inform their use in real-world applications. Credit not allowed for both CS 345 and DSCI 445 (Statistical Machine Learning).
CS 220 (Discrete Structures and their Applications); CS 150B (Culture and Coding: Python (GT-AH3)) or CS 152 (Introduction to Programming (CS0)-Python) or CS 165 (CS2 Data Structures) or DSCI 235 (Data Wrangling); MATH 155 (Calculus for Biological Scientists I) or MATH 156 (Mathematics for Computational Science I (GT-MA1)) or MATH 159 (One Year Calculus IB (GT-MA1)) or MATH 160 (Calculus for Physical Scientists I (GT-MA1)); STAT 301 (Introduction to Statistical Methods) or STAT 302A (Statistics Supplement: General Applications) or ECE 303 (Introduction to Communications Principles) or STAT 303 (Introduction to Communications Principles) or STAT 307 (Introduction to Biostatistics) or STAT 315 (Intro to Theory and Practice of Statistics); All prerequisite courses must be completed with a grade of C or better.; Credit not allowed for both CS 345 and DSCI 445.
Please check the CSU Bookstore for textbook information. Textbook listings are available at the CSU Bookstore about 3 weeks prior to the start of the term.
9704914068 | Asa.Ben-Hur@colostate.edu