Instructor: Jason Corso (UBIT: jcorso)
Course Webpage: http://www.cse.buffalo.edu/~jcorso/t/CSE555 or
http://www.cse.buffalo.edu/~jcorso/t/CSE455 but this is just a link to the first one.
Meeting Times: TR 11:00-12:20
Location: Davis 101
455 Recitations (mandatory registration): M 10-11 (Bell 337), W 8-9 (Park 250)
555 Recitation (voluntary) W 9:15PM-10:15PM (Davis 101).
David Johnson (ubit: davidjoh)
Yingbo Zhou (ubit: yingbozh)
- Instructor: TR 12:20-2:00 (Davis 332)
- TA: David Johnson: M 2:00-4:00 and R
Yingbo Zhou TW 4:00-6:00
Final Exam: Friday 3 May 2013,
11:45–2:45 in Davis 101.
Mailing List: email@example.com for both 455 and 555 students.
Student Updates: All updates will be posted to the course website and sent to the mailing list. There is no other official course announcement mechanism, such as a newgroup, blog, piazza or otherwise.
A Note On Contacting The Instructor: You are encouraged to contact the instructor or TA via the course-wide mailing list via email. You must 1) send the email from your UBIT account (since this is the approved one on the mailing list) and 2) include [CSE555] at the beginning of the command-line (even if you are in CSE455). Email that does not follow these conventions will not be read.
- Homework solutions for PCA, Boost and HMM posted. All quiz
- All past midterms and finals are available here, with
solutions where available.
- Videos from a past offering of the course are available at http://its.buffalo.edu/services/capture/links/previous/CSE555_PT.htm.
They are not indexed by topic, but we are doing that now and will
post links when available.
- All slides available. No more homeworks will be posted.
- HMM and Boosting homeworks now available.
- Slides from Gao guest lecture and on boosting are posted.
- Homework on PCA and dimensionality posted.
- GRADES Summary Stats
- March 3 -- Homeworks to parametric and nonparametric techniques
are posted. Quiz solutions to 4-7 are posted. (Note, some quizzes
do not have the Q link working, but the A link will work and contain
also the questions.)
- Feb 12 -- Solutions to quiz 4 posted.
- Feb 7 -- Solutions to homework 3 posted.
- Feb 5 -- Posted solutions to homework 2 and quiz 3. Posted new
homework 3 (on discriminants and SVM). Quiz this week will be
review and short answer, and include ideas from last Thursday's
guest lecture but no mathematical details.
- Feb 5 -- Posted matlab code for linear discriminants and linked
to the Python code for it. Linked to Ng's videos for linear
discriminants and SVMs.
- Small fix and update to the lecture slides on decision trees
(relating to Gini impurity). Solutions to Quiz 2 posted and graded
papers available for pickup from TAs.
- Quizzes available for download too.
- Jan 24 - Slides for Bayesian Decision Theory posted. (We're on
schedule!) Homework 1 solutions and homework 2
will be posted this
afternoon are posted too.
- Jan 18 - Annotated slides from in-class discussions are available for
download at the calendar below. Homework on decision trees and
random forests posted. pdf.
Previous Exams are also available (for study material)
Midterm 2009 |
Midterm 2010 |
Midterm 2011 |
Midterm 2012 |
- Jan 15 -- First Class.
- RECITATIONS ARE BEING HELD IN THE FIRST WEEK OF CLASS. These
are review sessions for the background material and highly
- Jan 9 -- A recitation time has been set up for the 555
students. It is voluntary but recommended. Wednesday nights
9;15-10:15 in Davis 101.
Before the course begins, you should review your linear algebra, probability and
statistics and calculus notes in preparation for the course.
Lectures from Ng's online ML course reviewing linear algebra
You should also reacquaint yourself with Matlab (or Octave) and
Python/Scipy/Numpy as many examples and voluntary assignments
are given in these language/environments.
Ng's tutorial on Octave (a free Matlab-clone) are
Python resources are at http://www.cse.buffalo.edu/~jcorso/t/CSE555/python_resources.html .
In order to deepen an understanding of the discussed math and
algorithms, students are strongly encouraged to work on their own to
complete the assignments and programming materials. A variety of
codes will be distributed to the course, in both Matlab and Python
Matlab and Octave are readily available on campus.
Students are recommended to learn and use Python (i.e., SciPy, NumPy)
in the course. Many
programming materials given in lecture and many programming aspects of the homeworks will be given in Python. A brief introduction to scientific
Python will be given in the course, but it is the students’ responsibility to get up to speed. Additional python resources will be maintained at
No work in Java, C/C++, OCaml or
other programming environment is contained in this course.
To allow for a common Python environment, the course will officially rely on the Enthough Python Distribution (EPD)
http://www.enthought.com/products/epd.php, which is easy to get, free, and includes the packages needed for our material. The
course will use EPD version 7.3. Students are encouraged to install it on their own computers, and it is also installed on the CSE network
(see https://wiki.cse.buffalo.edu/services/content/enthought-python-distribution for more
The professor will make all of the source code discussed in class
available to the students. In addition, some pieces of source code
will be provided as part of the homework assignments.
The source code discussed in the class and the core package is
accessible to the students in three ways
Note, the source code will be updated periodically throughout the
semester and you need to get the latest versions
- Via the web: http://www.cse.buffalo.edu/~jcorso/t/555code/
- On the departmental (student) Unix network:
/home/csefaculty/jcorso/555code. You can copy the whole
directory with rsync: rsync -Cavuz
- The directory is actually a bzr repository to which you
should have read access. So, you can just pull a copy of the
repository (you will not have privileges to commit) with bzr checkout
This option is particularly of interest because the code will be
periodically updated throughout the semester and you will want to have
the most recent version.
Also, note that toy data is included at the above location as well.
It is not in the repository, however.
No work will officially be submitted as it is home study.
Main Course Material
Please download the syllabus for all other information
regarding the course, including catalog description, grading