E E 461P E E 461P. Data Science Principles. 4 Hours.
Principles of unsupervised and supervised learning; exploratory data analysis; feature engineering; predictive modeling for regression and classification; clustering algorithms; neural networks and stochastic gradient descent methods; scalable models for Big Data sets; case studies; programming predictive models in Python and R. Four lecture hours a week for one semester. Only one of following may be counted: Electrical Engineering 379K (Topic: Introduction to Data Mining), 361M, and 461P. Prerequisite: The following with a grade of at least C- in each: Mathematics 340L, and Computer Science 314 or 314H, or Electrical Engineering 360C, and Biomedical Engineering 343 or Electrical Engineering 313, and Biomedical Engineering 335 or Electrical Engineering 351K or Mathematics 362K.
Students seeking the Bachelor of Science in Electrical Engineering pursue one of two curricula—electrical engineering or computer engineering. Both curricula contain the fundamentals of electrical engineering and computer engineering; they differ in technical core requirements in order to suit different career objectives.