Machine Learning

Information

• Time: T/TH 9:30 AM -- 10:45 AM
• Location: 1111 Humanities Building
• Instructor: Josiah Hanna

• TA: Adam Labiosa

• TA: Hadi Khader

Course Description

This course is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics, and algorithms of machine learning. Topics covered include supervised learning (neural networks, support vector machines, generative/discriminative learning), unsupervised learning (clustering, GMM, PCA), and reinforcement learning. The course covers theoretical concepts such as inductive bias, generalization, the PAC learning framework, etc. Assignments include some written exercise and short programming experiments with various learning algorithms.

Prerequisites

Students entering the class are expected to have a background knowledge of probability, linear algebra, and calculus, and have good programming experience. The course will not provide a review on the background knowledge, or tutorials on programming.

Announcements

• IF YOU ARE ON THE WAITING LIST: The capacity of the class is limited due to logistics. If the class is fully subscribed, you may want to consider the following options:
  • Come to the first several lectures and see how the course develops. We will admit as many students from the waitlist as we can, once we see how many registered students drop the course.
  • Take the class when it is offered again in another semester.