time: Wed 12:00-15:00
location: Sangsang Hall, Room 624
Prof. Seong-Eun Kim (email@example.com)
office hours: We are currently holding the office hours.
collaboration policy: Copying solutions from other students or other resources (e.g. the web or from students who have taken the class in previous years) is NOT allowed. Making answers to homeworks or exams available to others either directly or by posting on the web is also NOT allowed. We will not have a sense of humor about violations of this policy!
links: We will use eClass for questions, communication, and to submit assignments. All materials will be posted on the course website.
attendance: We expect students to attend classes regularly; However, please do not come to class if you are not feeling well or test positive for Covid-19. We will provide course recordings and lecture notes for students unable to make it to class.
description: Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application. It will introduce supervised learning (linear and logistic regression, decision trees, neural networks and deep learning, and Bayesian networks), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning.
homework (20%): There will be 6 homework assignments
midterm exam (30%):
final exam (30%): There will be a 2 hour long final exam during finals period, covering content from the entire course.
quizzes (10%): There will be 12 (roughly weekly) quizzes testing basic understanding of the material covered that week; You will receive full credit if you correctly answer at least 50% of the questions.
participation (10%): You are expected to participate by coming to class, asking/answering questions on eClass.