RIT Department of Computer Science

Description

Course Outcomes/Objectives

• 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 Contact

Grader Information

Lectures:

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.
• Late homework submissions will be accepted up to 24 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 (i.e. please no Visual Studio projects, etc). 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:
  • "The Elements of Statistical Learning" by Hastie et al. (TEoSL)
  • "Deep Learning" by Goodfellow et al. (DL)
  • Machine Learning for Dummies" by Mueller et al. (MLfD)
• Selected research papers

Additional Resources

• The Matrix Cookbook, a very, very handy reference you will want to have on hand.
• 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

• 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

  RIT's Academic Integrity Policy, Honor Code, and Student Conduct Policy. 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.

• Policy on Large Language Models

  The policy on using large language models (LLMs), part of a broader class of statistical learning models labeled as "generative AI", for this course is simple -- please read the above strict policy "Academic Integrity" for this class. Using an LLM to write your code/text will be treated as not producing your own work (such as copying one of your classmates' work) and will be handled accordingly -- you must produce work that is completely your own. Adhering to this policy is further for your personal benefit -- you get what you put into this class, and to master the craft of machine learning, you must work through the mathematics and do the thinking for yourself in order to truly develop the machine learning literacy this class aims to provide.