CSCI-739 Topics in Intelligent Systems: Intro to Machine Learning
Fall Semester 2018 (2181)
An introduction to machine learning
theories and algorithms. Topics include Supervised Learning
(e.g. Regression, Deep Neural networks, and SVM) and Probabilistic
Graphical Models (e.g. Bayesian networks and Markov models).
Programming assignments and projects are required.
Students will be able to describe the types of problems that
machine learning techniques are used to solve, and which machine
learning algorithms are appropriate for solving each type of
Students will be able to describe, compare, and contrast different
machine learning algorithms.
Students will be able to implement machine learning algorithms
using labeled data.
Students will be able to work as a team to implement solutions to
complex, real world machine learning problems.
Students will be able to describe evaluation techniques for
assessing and comparing machine learning techniques.
I am usually good at answering emails promptly, however,
there is no guarantee I'll respond during
the evening or on weekends.
I will not answer homework-related questions the day the
assignment is due.
Monday 2:00-4:00, Wednesday 9:30-11:30, or by appointment.
Tuesday 2:00-3:15, GAN-4202
Thursday 2:00-3:15, GAN-4020
Office hours: M/W 11:00-12:00, CS Grad Lab
Friday, 1-1:50 OR 2-2:50, BRN-1110
The homework assignments (not projects) in this course are to be
done on your own. You may discuss the homeworks in the general
sense with your classmates, tutors, and the instructor. That is,
no pictures for later, no shared notes, no shared code.
Looking up the solutions
online or in the literature, are not
permitted. You are encouraged to discuss any class material and
homeworks whose deadline has passed with your peers, in the
tutoring center, with the instructor, or anybody else whom you make
The projects will be done in groups of 2 students - for this
work, the same restrictions apply as above, except to the group
rather than the individual.
Late homework submissions will be accepted only up to 48 hours
after the deadline, with a 20% penalty.
Handing in your homeworks: All homework, both written and code,
may be handed in via MyCourses. Written homework may also be handed
in as hardcopy at the beginning of class on the day it is due. Note
that for all programming homeworks/labs, they must be (easily)
executable on the CS lab Linux machines. More details on allowed
and available libraries etc will be given with each assignment
(what is allowed for some may not be allowed for others).
The final exam will be closed book, closed notes. You may
prepare one letter-size hand-written "summary sheet" (no
Homework or exam grade can be disputed within one week
after the graded work is handed back. Dispute the grade with the
instructor, not the grader. Your grades will be posted on MyCourses.
This includes information
about reading and homework assignments, quizzes, exams, etc.
Homework assignments (5)
CS Common Course Policies Include:
Rescheduling an Exam
Exams can not be made up except for real emergencies in which
case proper documentation (like a doctor's note) will be required.
If at all possible, you should contact me prior to the exam. Oversleeping,
cars that don't start etc. do not constitute a valid excuse.
Academic Senate revised the Final
Examination Policies on March 28, 2013. Please refer to the policies
for related questions.
During the add/drop period,
you may drop this course and it will
disappear from your transcript. After that time, you can only
withdraw from the course; the course will appear on your transcript
with a grade of W.
See the institute's calendar
regarding the add/drop period and latest withdrawal date.
RIT is committed to providing reasonable accommodations to students
with disabilities. If you would like to request accommodations such as
special seating or testing modifications due to a disability, please
contact the Disability Services Office. It is located in the Student
Alumni Union, Room 1150; the web site is
After you receive accommodation approval, it is imperative that you
see me during office hours so that we can work out whatever
arrangement is necessary.
DCS Policy on Academic Honesty will be enforced.
You should only submit work that is completely your own.
Failure to do so counts as academic dishonesty and so does
being the source of such work. Submitting work that is in large part not
completely your own work is a flagrant violation of basic ethical behavior
and will be punished according to department policy.