Machine Learning

CSCI 567, Fall 2018

Haipeng Luo

TA: Chin-Cheng Hsu (chincheh), Shamim Samadi (shamimsa), Chi Zhang (zhan527), Ke Zhang (zhan355)
Course Producers: Ashir Alam (ashirala), Malhar Kulkarni (mskulkar), Zeyu Li (lizeyu), Collin Purcell (collinpu), Kishore Venkateshan (kishorev), John Zeiders (zeiders)
Emails: "string inside the parentheses"@usc.edu
Office Hours: see Piazza "Resources->Staff"

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 in linear algebra, calculus and multivariate calculus, basic probability and statistics; (2) Skills in programming with Python (self-studying scikit-learn and related packages is expected); (3) Basic skills in using git for maintaining code development. In addition, an undergraduate level course in Artificial Intelligence may be helpful but is not required.

Grading: 5 written assignments (15%) + 5 programming assignments (25%) + 2 exams (60%). Details can be found here.

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

Discussions: Attending the discussion sessions is required (they start from the second week). The discussion provides more detailed and in-depth exposition of the lectured materials.

Communication: The main communication tool for this course is Piazza. Please sign up via this link. Messages that do not need a particular instructor's direct attention should be posted to Piazza with the appropriate privacy setting. Students are encouraged to participate in the discussions on Piazza actively.

If you email your instructor, you must include the substring "CSCI 567" to begin a meaningful subject line and have tried to resolve the issue appropriately otherwise (e.g., questions about course material should be posted to Piazza first, using email only after an appropriate amount of time has passed without a response). Such emails must be sent from your USC email account.

Other useful resources:
ML materials:

• A First Encounter with Machine Learning by Max Welling
• Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan
• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• A Course in Machine Learning by Hal Daume III
• Bayesian reasoning and machine learning by David Barber
• Pattern Recognition and Machine Learning by Christopher Bishop
• A tutorial by Andrew Moore

• The Matrix Cookbook by Kaare Brandt Petersen and Michael Syskind Pedersen
• Calculus by Gilbert Strang
• Linear Algebra by Gilbert Strang
• Introduction to Probability and Statistics by Jeremy Orloff and Jonathan Bloom

• The official Python Tutorial
• LeetCode

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 me (or to TA) 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.