CSCI 335: Machine Learning
An introduction to both foundational and modern machine learning theories and algorithms, and their application in classification and regression. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), Bayesian decision theory, parametric and non-parameteric classification models (e.g., SVMs and Nearest Neighbor models) and neural network models (e.g. Convolutional, Recurrent, and Deep Neural Networks). Programming assignments are required.
Prerequisite(s): (CSCI-243 or SWEN-262) and (MATH-251 or STAT-205), or equivalent courses.
Students may not take and receive credit for CSCI-335 and CSCI-635. If you have earned credit for CSCI-635 or you are currently enrolled in CSCI-635, you will not be permitted to enroll in CSCI-335.
Credit Hours: 3
Lectures: Tuesday and Thursday 2:00pm - 3:15pm. Lectures will be both in-person and over Zoom, and will be recorded with links to recordings available through MyCourses.
1. 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 problem. (Assignments, Quizzes)
2. Students will be able to describe, compare, and contrast different machine learning algorithms. (Assignments)
3. Students will be able to implement machine learning algorithms. (Assignments, Projects)
4. Students will be able to work as a team to implement solutions to complex, real world machine learning problems. (Projects)
5. Students will be able to describe evaluation techniques for assessing and comparing machine learning techniques. (Assignments, Projects)
Important Note: Update (Oct 24, 2023): Questions about assignments or other assigned work will not be answered:
( 1 ) after office hours finish on Friday when assignments are due Friday, and
( 2 ) at any point the day that an assignment is due, if it is due other than a Friday (when office hours are held). Questions about assignments or other assigned work on the day that they are due will not be answered.
Contact information for the instructor is available through the Contact page.
Office Hours: Office hours will be held in-person in Prof. Zanibbi's office (GOL 3551) and over Zoom and Discord. Zoom links are available through MyCourses. Office hours will not be recorded.
Class discord (online chat): Please use the discord channel to ask clarifying questions about assignments and course material, and for general discussion about the course. The instructor will try to respond within 24 hours during the work week (roughly until early Friday afternoon). Do not use the discord channel for anything other than discussions about the course with your instructor, TA, and classmates.
Email: The instructor will try to respond to emails within 24 hours. However, email received on Friday afternoons and weekends may not receive a reply until the following Tuesday.
RIT Attendance Policy. Attendance requirements are described in RIT Policy D0.4.0 Attendance. Some key details:
- Students are expected to attend all classes on-time.
- Students must make arrangements in advance of absences in order to fulfill course requirements.
- Students do not need to file excuses for absences.
- Instructors are not required to maintain attendance records, but must report prolonged absences to the student's advisor or department.
Illness. In the event of illness, students should continue to notify faculty directly that they will need to be absent and when they anticipate being able to rejoin the class. Per Policy D04.0 – Attendance, students are still responsible for fulfilling normal course requirements during their absence. Students are not required to provide details about or documentation related to health-related absences.
Rescheduling an Exam. Final projects completed during exam week cannot be made up except for real emergencies in which case proper documentation (like a doctor's note) will be required. Please see RIT's Academic Senate Final Examination Policy for related questions.
Important Note: Writing and code for assignments and projects may not be produced using generative AI tools.
Grading Criteria. For full points, deliverables in the course including question answers, code, presentations and write-ups must be:
(1 ) correct and complete, i.e., all parts of a question are answered with no errors and no omissions,
(2) justified, if an explanation is asked for,
(3) clear, i.e., understandable with a reasonable effort, and
(4) in the requested format, including both the forms of answers. For example, not providing bullets when prose is asked for, and submitting work with the required file types (e.g., providing a PDF as asked, rather than a Word file).
MyCourses. To help students follow their progress in the course, all grades will be posted on MyCourses, including an automatically updated final grade based on completed work, along with class averages and grade distributions for all graded items.
Grade Adjustments. All grades may be disputed within one week after graded work is returned. Discuss any grading concerns that you have with the instructor, and not the TA or grader.
10 quizzes will be given out weekly beginning in Week 2 of the semester. The two lowest quiz grades will be dropped.
Quizzes will be available through a "Quizzes" link in MyCourses. Students are permitted to retake a quiz as many times as they like, and will receive the highest score that they receive across these attempts before the deadline. Students will have at least one day (24 hrs) to complete each quiz.
5 assignments will be given, beginning in Week 3 of the semester.
Assignments involve both writing and programming questions. Students are expected to follow submission instructions as provided in the assignments carefully.
Instead of an exam, students will complete a group project at the end of the semester in groups of 3 students. The project involves designing, executing, and reporting on an experiment with a machine learning model.
The first deliverable for the project is an experiment design, and a draft of the final experiment report and materials to be delivered for the final project.
Instead of an exam, students will complete a group project at the end of the semester in groups of 3 students. The project involves designing, executing, and reporting on an experiment with a machine learning model.
The final deliverable for the project are the experimental results, code, and experiment report (20%), along with a short 5-10 minute presentation given during the exam slot (5%).
90% A- 93% A
80% B- 83% B 87% B+
70% C- 73% C 77% C+
60% D
< 60% F
Quizzes: The two lowest quiz grades are automatically dropped. Quizzes cannot be re-scheduled, and cannot be submitted late unless permission was granted by the instructor before the quiz deadline.
Assignment & Project Proposal: Late submissions will be accepted up to 1 week after the deadline with a 10% penalty.
Final Exam and Final Project Deliverables: No late submissions will be accepted for the final project deliverables during the Exam period.
If you require accommodations, please let the instructor know so that we can be of assistance.
RIT ADA Statement. The Disability Services Office is dedicated to facilitating equitable access to the full RIT experience for students with disabilities. We value disability as diversity and work in collaboration with campus partners to foster a welcoming, diverse, and inclusive campus community.
Any RIT student with a permanent or temporary disability can register and request accommodations with the Disability Services Office. Accommodations are determined on a case-by-case basis via a student-centered process, taking into account what is most appropriate and reasonable for an individual student. Visit www.rit.edu/dso to learn more.
Individual and Group Work. Assignments and quizzes are to be completed on your own. You may discuss these with your classmates, the TA, and the instructor, but you must create all submitted work for assignments on your own. It is not acceptable for a student to prepare an answer set and share this with other students. Where work is done by groups of students, the same restrictions apply as for individual work (i.e., groups may discuss their work, but not provide material for use by other groups in their submissions and presentations).
Lecture. This course covers a wide variety of topics, some being complex and/or counter-intuitive. Students should raise their hands to ask clarifying questions, to check their understanding, or to share an idea. Sometimes the instructor will not call on the student right away to make sure that the course progresses at a reasonable pace. Students are always welcome to send questions over email, discord, or talk to the instructor during office hours (see top of page).
Readings. Students are expected to complete assigned readings, and should expect questions from readings that were not covered in lecture to appear in assigned work (e.g., quizzes, assignments).
Academic Integrity. As an institution of higher learning, RIT expects students to behave honestly and ethically at all times, especially when submitting work for evaluation in conjunction with any course or degree requirement. All students are encouraged to become familiar with RIT's Academic Integrity Policy, Honor Code, and Student Conduct Policy.
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.