IE 398 ML

Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Prerequisites: CS 101, IE, 300, MATH 241, and MATH 415 (or the equivalents). Course Description: This course is an introductory/intermediate level machine learning course for senior undergraduate and junior graduate students. This lecture course will mainly focus on machine learning algorithmic development (and some theoretical analysis for the second part of the course). The students should have programming skills to implement the algorithms. This course contains two parts: (1) In the first part, we will discuss the basics of supervised (regression and classification) and unsupervised learning (clustering and dimension reduction). Then, we will learn modern topics such as graphical models, EM algorithm, neural networks, semi- supervised learning, and stochastic optimization for training web-scale data. We will unveil the blackbox for each machine learning algorithm and provide the details on how the algorithm was developed. (2) In the second part, we will move from machine learnin