Information about graduating with Honors in M&CS
Before declaring Honors, please read carefully!
Honors - The Math & Comp Sci honors program encourages intensive study
in an area of mathematical science in addition to meeting the requirements
for the major.
The honors program is designed to encourage a more intensive study of
mathematical sciences than the B.S. program. In addition to meeting all
requirements for the B.S., the student must:
- Maintain an average letter grade equivalent to at least a 3.5 in all
- Complete at least 15 units in mathematical sciences in addition to the
requirements for the major listed above. Include in these 15 units at
least one of the following:
- An approved higher-level graduate course
- Participation in a small group seminar
- At least 3 units of directed reading
- Prepare a statement describing major area of concentration for honors
- Describe how each course selected added to the student’s knowledge
and understanding in area chosen for concentration.
- Students interested in honors should consult with their
by the last
quarter of their junior year
to prepare their program of study. Honors
work may be concentrated in fields such as biological sciences,
environment, physics, etc.
- Suggested electives for students pursuing Honors: EE 364, CME 206,
CS 229, CS 248, Math 171, MATH 172, STATS 202, STATS 216, STATS 217.
If you want to declare departmental honors, log on to Axess, click
"Declare Major/Minor" and choose MCS-Honors.
You will submit a request to the department in which you want to
pursue departmental honors. The department will inform you of their
decision whether to accept you into the honors program
Note: Departmental honors must be declared and approved no later than the application to graduate deadline for the term in which the student intends to graduate.
Previous Student work includes concentration in
- CS theory,
especially different types of algorithms and algorithmic paradigms;
detecting the difference between human and non-human responses in
game-theory type games;
- demonstrating Bayesian models and networks
potential in a legal analysis context to evaluate real-world applicability
of Bayesian networks to criminal law and civil litigation;
analyzing phylogenetic trees of bacterial data.