Class policies



Class Policies
CS B553: Algorithms for Optimization and Learning



Kris Hauser

Office: Informatics East, 257 (connector building)

Phone: 856-7496

Email: hauserk at

Office hours: Wednesday 10-11am


CS B551 or equivalent, calculus, and linear algebra.

This course is aimed at Masters students, beginning PhD students, and high-level undergraduates in computer science, as well as students in related fields who have a strong computing background.

Experience with computer programming, data structures, and algorithms will be assumed (CS C343 equivalent). Programming will be performed in the language of the student’s choice.  We recommend Matlab, Python (with the numpy/scipy packages), and C++ with an appropriate numerical library (e.g., GSL).


The first half of the class will primarily consist of lecture notes. For the second half of the class we will use Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman.

Meeting times

Classes meet on Tuesdays and Thursdays from 11:15AM-12:30PM in Info East, room 122.


Everyone is responsible for reading the departmental statement on academic integrity before starting the first assignment.

Homeworks, including programming projects, are to be completed individually. You may discuss the material with other students, but all written work must be your own.

Homework assignments are due by the end of class on the due date unless otherwise specified. Extensions are granted only in extenuating circumstances at the discretion of the instructor. Requests for extensions require advance notice (except for emergencies).

Late homeworks are deducted 10% for each day late.

Email policy

Questions can be directed by email to the instructor. Please begin the subject line with "B553". Email will be responded to within 24 hours, unless otherwise noted. Please allow sufficient time for responses before assignment deadlines.

Grading policy

The final grade will be comprised primarily of homework (80%) scores, with additional components from quizzes (10%) and participation/attendance (10%).  There will be 8 homework assignments.

Optional Final Project

Students that intend to continue coursework or research in AI-related fields are highly encouraged to pursue a final project. The project is optional. Projects may be done in a number of ways, such as applying optimization and learning techniques to real-world problems, writing a research survey, and reproducing results from recent research papers. More information will be provided in class.


Students who opt to complete a final project will produce an original technical report as well as a 10 minute in-class presentation near the end of the semester. The final project will substitute for 4 homework assignments of the student’s choosing (40% of the overall grade).