RIT Department of Computer Science

CSCI-736 Neural Networks and Machine Learning (Neural Computation): Syllabus

Spring Semester 2023 (2225)

NOTE: This page is still under a bit of construction and will be subject to change...

Description

The course will introduce students into the current state of artificial neural networks. It will review different application areas such as intrusion detection and monitoring systems, pattern recognition, access control and biological authentication, and their design. The students will be required to conduct research and analysis of existing applications and tools as well as to implement a course programming project on design of a specified application based on neural networks and/or fuzzy rules systems. (CSCI-630 or CSCI-331 or permission of instructor).

Course Outcomes/Objectives

Students will be able to advance their theoretical knowledge of artificial intelligence methodologies in different applications.
Students will be able to learn the structure and design concepts of neural networks applications.
Students will be able to advance their communication and presentation skills.
Students will be able to advance their research and analytical skills.

Instructor Contact

Alexander G. Ororbia II
Office: GOL-3537
E-mail: ago AT cs DOT rit DOT edu
Office Hours: By Appt
Website: http://www.cs.rit.edu/~ago

I am usually good at answering emails promptly, however, there is no guarantee that I will respond during the evening or on weekends. I will not answer homework-related questions the day the assignment is due. If you have questions and I or the grader are not available, I highly recommend that you stop by the tutoring center (see below).

Grader Information

Aakriti Lnu E-mail: al1745 AT rit DOT edu Office Hours: TBA

Lectures:

Tues/Thurs 3:30PM - 4:45PM, Golisano Hall (GOL)-2690

Course Policies

All homework in this course, other than the research report and presentation, is 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.
Discussions with anybody else, including looking up the solutions online or in the literature other than the course book, 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 might make listen.
The research project/report will be done in groups of 1-3 students - for this work, you may talk to other people as well but of course the report, code, and presentation must be completely developed by the group members themselves. More details on this work will be forthcoming.
Late homework submissions will be accepted up to 48 hours beyond the deadline, for a 20% grade deduction. No exceptions unless a true emergency arises (proper documentation is required in such cases).
Handing in your homeworks: All homework, both written and code, must be submitted through via MyCourses. Note that for all programming homeworks/labs, they must be (easily) executable on the CS lab Linux machines. Programming must be done primarily in Python (but R/MatLab are permitted for statistical analysis/visualization purposes).
Quizzes are closed book, closed notes.
Homework or quiz grades 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.
The quizzes cannot be made up unless a true emergency arises (proper documentation is required in such cases).
Hopefully there is no need to link to the departmental policy on academic honesty, but it will be enforced if necessary.

Course Materials

Some texts that we will use include:
- "Deep Learning" by Goodfellow et al. (DL)
Some texts that might help you with respect to basics in ML:
- Machine Learning for Dummies" by Mueller et al. (MLfD)
Selected research papers

Additional Resources

The Matrix Cookbook, a very handy reference you will want to have on hand. (Those of you who have taken courses with me before know that I like/often refer to this resource!)
The Course Schedule, including information about reading and homework assignments, quizzes, etc., will be linked from the course web page. I will also make any slides used available here after the relevant session.

Grading

Component	Weight
Semester Project (including Proposal)	45%
Research Presentations (2-3)	35%
Feedback/Participation	20%

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. No exceptions!

RIT's Academic Senate revised the Final Examination Policies on March 28, 2013. Please refer to the policies for related questions.
Course Withdrawal
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.
Disability Services
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 www.rit.edu/dso. 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.
Academic Integrity
The 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.

Updated: January 4, 2023