CMSC 858L, Foundations of Machine Learning, Fall 2015: Tue, Thu 2-3:15PM

Administrative Details

Instructor: Aravind Srinivasan
Office: AVW 3263, Phone: 301-405-2695
Instructor's office Hours (starting Sept 3rd): Tue and Thu 10-11 in AVW 3263 (additional slots by appointment; Aravind is happy to talk to students)
Course Time and Location: Tue, Thu 2-3:15PM, CSIC 3120
Half-TA: David G. Harris
Course Webpage:

Course Details

Course material and topics: There will be no required textbook for this course: we will primarily use Rob Schapire's excellent scribed course notes. The book by Shalev-Schwartz and Ben-David (also available free online here) is also very useful.
Based on time available, we also plan to cover additional topics including kernels, basic optimization techniques, information theory, and ethical issues. This being a "foundations" course, the emphasis will be on rigorous algorithms and proofs; we will also discuss practical heuristics & applications and ethical considerations. Check out Samuel Ieong's notes and Srinath Sridhar's two videos for relevant probability background.

Please click here for the regularly-updated list of topics covered in past classes.

Problem-solving: The best way to learn the material is through problem-solving. We will facilitate this via a partially-inverted classroom format: since we will closely follow Schapire's notes for much of the class, students will be expected to read ahead from the notes (read from start to end, a bit ahead of class). We will go over the notes in class faster than in an introductory graduate class, and use the resultant time to do some problem-solving during Thursday classes.

Homework and Project: We will have some graded and some ungraded homework assignments. Students will work in groups of three for graded homework assignments, and are also urged to complete the ungraded assignments (solutions to which will be provided).
The project will involve reading substantial material and problem-solving based on it; alternatively, student-groups are welcome to propose to Aravind new projects that they will do based on the course material -- please make such proposals by October 1st, and Aravind will respond by October 6th whether your proposal is approved or not.

Grading and participation: We will have a take-home mid-term and in-class final. The grade will be determined by: Homework 30%, Mid-term 25%, Final 35%, and Class Project 10%. Enthusiastic participation is strongly encouraged. Much of the outside-classroom discussion will be through Piazza; please register and participate.

Exams: The final will be during the university's official time: in our classroom CSIC 3120, 10:30AM-12:30PM on Thursday, Dec 17th. The final will include everything covered during the semester: you can bring your own notes, HW solutions, Schapire's notes, and handouts given in class - no other material is allowed. The mid-term will be take-home: posted on 10/22, due back by 11:59PM 11/1.

Qualifying areas: The course will be valid for Ph.D. qualifying coursework, M.S. qualifying coursework, and M.S. comps, all in the Algorithms and Computation Theory and AI areas; the relevant exams will be the mid-term and the final.

General Info.: Class participation is strongly encouraged; students are urged to come to the office hours if they have questions, and can also email Aravind to setup alternative times if they cannot attend the regular office hours. A few lectures will be rescheduled (or covered by guest lectures) during Aravind's travel; very few of the office hours may also be canceled. (Again, students are always welcome to email Aravind to setup additional meeting-times as needed.)  

Excused Absences

See the university's policy on medically-necessitated absence from class. The "Major Scheduled Grading Events" for this course are the mid-term and final exams; students claiming an excused absence from these events must apply in writing and furnish documentary support (such as from a health-care professional who treated the student) for any assertion that the absence qualifies as an excused absence. The support should explicitly indicate the dates or times the student was incapacitated due to illness. Self-documentation of illness is not itself sufficient support to excuse the absence. An instructor is not under obligation to offer a substitute assignment or to give a student a make-up assessment unless the failure to perform was due to an excused absence.

Academic Accommodations for Disabilities

Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide, to the instructor in office hours, a letter of accommodation from the Office of Disability Support Services (DSS) within the first two weeks of the semester.

Academic Integrity

The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council. This Code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit

To further exhibit your commitment to academic integrity, remember to sign the Honor Pledge on all examinations and assignments: "I pledge on my honor that I have not given or received any unauthorized assistance on this examination (assignment)."