RIT Cognitive Science

Description

Course Outcomes/Objectives

• Students will be able to describe basic, key concepts of numerical computation and analysis, optimization, and sampling. Evaluation: written assignments, class participation/discussion, quizzes.
• Students will be able to express problems in terms of numerical optimization/solution searching. Evaluation: written assignments, class participation/discussion, quizzes.
• Students will be able to apply key numerical solvers and techniques for tackling issues in numerical computation. Evaluation: written assignments, class participation, (research) project / oral presentation.

Instructor Contact

Lectures:

• Tues/Thurs 3:30-4:45pm, Wallace Library (WAL)-4530

Course Policies

• All homework in this course, 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.
• For the research project/report, 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 (i.e. please no Visual Studio projects, etc). Programming must be done primarily in Python.
• Homework grades can be disputed within one weeks after the graded work is handed back. Your grades will be posted on MyCourses.
• Hopefully there is no need to link to the departmental policy on academic honesty, but it will be enforced if necessary.

Required Materials

• Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib (NP), 3rd Edition, Robert Johansson

Additional Resources

• The Matrix Cookbook, a very handy reference you will want to have on hand.
• The Course Schedule, including information about reading and homework assignments, 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

  Quizzes 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.

• Policy on Large Language Models

  The policy on using large language models (LLMs), which are part of a broader class of statistical learning models often 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 for your personal benefit -- you get what you put into this class, and to master the basics of scientific computing, you must work through the mathematics and programming, doing the thinking for yourself in order to truly develop the competency and literacy that this class aims to provide.