Machine Learning (Spring 2025)

Overview

The chief objective of this course is to study standard statistical machine learning methods, including algorithms for supervised learning, unsupervised learning, reinforcement learning, and others. Particular focuses are on the conceptual understanding of these methods, their applications, and hands-on experience. A concrete schedule can be found here.

Prerequisites

• undergraduate level training or coursework on linear algebra, (multivariate) calculus, and basic probability and statistics

• basic skills in programming with Python

• in addition, an undergraduate level course in Artificial Intelligence may be helpful but is not required.

Requirements and Grading

• (40% of grade) 4 written assignments

• (40% of grade) 2 quizzes

• (20% of grade) 1 programming project

• Initial final grade cut-offs (for A and B) are: A=[92, 100]; A-=[86, 92); B+=[80,86); B=[75, 80); B-=[70,75). The actual final cut-offs will NOT be released. They might be different from the above but if so could only be LOWER (e.g., if you get 90, your final grade is AT LEAST A-). Final grades are non-negotiable. If you cannot accept this condition, you should not enroll in this course.

Textbooks

There is no required textbook for this course, but the following two books (both available online) are the main recommended readings:

• Probabilistic Machine Learning: An Introduction by Kevin Murphy

• Elements of Statistical Learning by Hastie, Tibshirani and Friedman

Discussion Sessions

The discussion sessions provide more detailed and in-depth exposition of the lectured materials, as well as reviews of homework and quizzes.

Communications

The main communication tool for this course is Piazza. Please sign up via this link. All announcements of this course will be made on Piazza, so you have to sign up. All questions/messages that do not need a particular instructor/TA/grader's direct attention should be posted on Piazza with appropriate privacy setting. Students are encouraged to participate in the discussions actively.

For all other questions related to a particular instructor/TA/grader, send us an email using your USC email account and include the word “CSCI 567” in the title.

Students with disabilities

Any student requesting academic accommodations based on a disability is required to register with Office of Student Accessibility Services (OSAS) each semester. A letter of verification for approved accommodations can be obtained from OSAS. Please make sure that the letter is delivered to the instructor as early in the semester as possible.

Academic integrity

Our goal is to maintain an optimal learning environment. You can discuss the homework/project at a high level with other students, but you should not look at any other student's solutions. Trying to find solutions online or from any other sources (including using chatbots such as ChatGPT) for any homework or project is prohibited, will result in zero grade and will be reported. To prevent any future plagiarism, uploading any material from the course (your solutions, exams etc.) to the internet is prohibited, and any violations will also be reported. Please be considerate, and help us help everyone get the best out of this course.