Lecture time: TTh 9:30-10:45am
Location: Online
Instructor: Prof. Elena Zheleva
Office hours: Tue 2-4pm, online
Contact:
TA1: Christopher Tran
Office hours: TBD
Contact: ctran29@uic.edu
This course provides an introduction to machine learning, the study of systems that improve automatically based on data and past experience. The course will introduce common machine learning tasks, such as classification and clustering, and some of the successful machine learning techniques and broader paradigms that have been developed for these tasks. Topics include but are not limited to decision trees, nearest neighbors, linear models, support vector machines, neural networks, ensemble methods, k-means, and graphical models. The course is programming-intensive and an emphasis will be placed on tying machine learning techniques to specific real-world applications through hands-on experience.
Working knowledge of probability, linear algebra, calculus, and ability to (learn to) program in Python.
We will use Piazza for the course schedule, discussions, and materials, and Gradescope for grading.
Python is the programming language used for homework assignments.
Primary: A Course in Machine Learning by Hal Daume III (available online)
Understanding Machine Learning: From Theory to Algorithms by Shai Ben-David, Shai Shalev-Shwartz (available online)