Machine Learning

CSCI 567, Fall 2020

Haipeng Luo

TAs: Liyu Chen (liyuc), Sungyong Seo (sungyons), Mengxiao Zhang (mengxiao.zhang)
Course Producers: Shubham Nagarkar (slnagark), Sowmya Penmetsa (sowmyadu), Anamay Sarkar (anamaysa), Dhiti Thakkar (dhitisam)
Emails: "string inside the parentheses"@usc.edu
Office Hours: see Piazza "Resources->Staff" (they start from the second week). General rule: for lecture and quiz related questions, go to TAs' office hours; for homework and project related questions, go to CPs' office hours.

D-clearance: There are a couple of open seats at this point. You can enroll without D-clearance.

Overview: The chief objective of this course is to introduce standard statistical machine learning methods, including but not limited to various methods for supervised and unsupervised learning problems. Particular focuses are on the conceptual understanding of these methods, their applications and hands-on experience.

Prerequisites: (1) Undergraduate level training or coursework on linear algebra, (multivariate) calculus, and basic probability and statistics; (2) Basic skills in programming with Python. In addition, an undergraduate level course in Artificial Intelligence may be helpful but is not required.

Grading: 5 written assignments (30%) + 2 quizzes (40%) + 1 programming project (30%). Details can be found here. Initial final grade cut-offs (for A and B) are: A=[90, 100]; A-=[85, 90); B+=[80,85); 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.

Textbooks: There is no required textbook for this course, but the following two books are the main recommended readings:

• Machine Learning: A Probabilistic Perspective [MLaPP] by Kevin Murphy
• Elements of Statistical Learning [ESL] by Hastie, Tibshirani and Friedman

Discussion Sessions: The discussion sessions are led by TAs and provide more detailed and in-depth exposition of the lectured materials, as well as reviews of homework and quizzes.

Communication: The main communication tool for this course is Piazza (no Slack). 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/CP's direct attention should be posted on Piazza with the appropriate privacy setting. Students are encouraged to participate in the discussions actively.

For all other questions related to a particular instructor/TA/CP, send us an email using your USC email account and try to include the substring "CSCI 567" to begin a meaningful subject line.

Students with disabilities: Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to the instructor as early in the semester as possible.

Academic integrity: USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one's own academic work from misuse by others as well as to avoid using another's work as one's own. All students are expected to understand and abide by these principles. Scampus, the Student Guidebook, contains the Student Conduct Code in Section 11.00. Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty.