DATA SCIENCE AND MACHINE LEARNING
Fundamentals of data science and machine learning. Topics include: introduction to scikit-learn, Keras and tensorflow2, linear and logistic regression, clustering, support vector machines, random forest trees, neural networks, deep learning, natural language processing. Graduate/Undergraduate Equivalency: CMOR 438 . Recommended Prerequisite(s): Three semesters of calculus recommended. A background in some programming language would be extremely useful. Mutually Exclusive: Cannot register for INDE 577 if student has credit for CMOR 438 .
Prerequisites & course information
Short Title: DATA SCI & MACHINE LEARNING Department: Industrial Engineering Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees. Course Level: Graduate
Sources
- Course catalogue ↗2026–2027