CS B553: Algorithms for Optimization and Learning
Office: Informatics East, 257
Email: hauserk at indiana.edu
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
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.
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
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).