|Instructor: Prof. Richard Zanibbi (Contact info)
Office Hrs: T/Th 2:00-3:20 GOL 3551
Lectures: T/Th 3:30-4:45pm GLE 3139
Teaching Assistant: Timothy Zee (Contact info)|
TA Office Hrs: M/W 11:00am-12pm GOL 3650
Tutorials: (from Wk 2) Fri 1pm & 2pm Brown Hall 1110
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 problem.
- 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.
Instructor ContactRichard Zanibbi
I try to respond to email within 24 hours. However, email received on weekends I will respond to the following Monday. I will also not answer homework or project-related questions the day they are due.
- Tuesday/Thursday 2:00-3:20pm, or by appointment.
- Tuesday 3:30-3:45, GLE-3139
- Thursday 3:30-3:45, GLE-3139
Lecture. This is an advanced graduate course, which will cover a wide variety of topics, some being complex and/or counter-intuitive. Students should raise their hands to ask a question whenever something is unclear, they want to check their understanding, or have an idea to share. Sometimes the instructor will not call on the student right away, in the interest of covering material and making sure that the course progresses at a reasonable pace. Students are always welcome to send questions over email or talk to the instructor during office hours (see top of page).
Homework. 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.
Projects. 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.
Grading. For full points, deliverables in the course including question answers, code, presentations and write-ups must be:
- Correct and complete (all parts/aspects of the question are covered).
- Jusitified: if an assignment or test question asks for an explanation or justification, it must be provided for full points.
- Clearly written: answers/reports should be written with care and attention to language, and provide the context needed to understand the answer with a reasonable effort. Note that the goal here is clarity, not complexity. Make your answer understood in simple terms wherever possible.
- Provided in the requested format. For example, files are submitted in the correct format, a question that asks for a written description is not a bulleted list, etc..
Homeworks, projects, and exam grade can be disputed within one week after the graded work is handed back. Discuss any concernts that you have regarding grades with the instructor, and not the TA.
Late Policy. Late homework submissions will be accepted only up to 48 hours after the deadline, with a 20% penalty.
Submissions. 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).
Final Exam. The final exam will be closed book, closed notes. You may prepare one letter-size hand-written "summary sheet" (no photocopies).
Academic Dishonesty. Hopefully there is no need to link to the departmental policy on academic honesty, but it will be enforced if necessary.
- No textbook is required. Notes and links to papers will be posted in MyCourses and/or on the course website.
- Some texts that we will use include:
The Course ScheduleThis includes information about reading and homework assignments, quizzes, exams, etc.
Component Weight Homework assignments (5) 45% Projects (2) 30% Final exam 25%
CS Common Course Policies Include:
Rescheduling an ExamExams 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.
RIT's Academic Senate revised the Final Examination Policies on March 28, 2013. Please refer to the policies for related questions.
Course WithdrawalDuring 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 ServicesRIT 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 IntegrityThe 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.