SCOPE OF GRADING

1. Reading Assignments and Video Assignments

The course schedule includes readings from the course textbooks and/or other resources. This information will be available at least two days before the lecture. The goal of the reading and/or video assignments is to prepare for class, to familiarize yourself with new terminology and definitions, and to determine which part of the subject needs more attention.

Each session may have a short quiz at the beginning which covers the assigned reading for that session which will be graded.

2. Quiz and Exercises

A session will have the following pedagogy layout which will be repeated around three times: approx. 15 minutes of live online instruction followed by approx 15 minutes of Q/A and/or Quiz and/or Exercises on the edStem platform. The level of paritcipation and correctness on these exercises will be graded.

3. Homeworks

There are three graded homework assignments. The assignments will be submitted in pairs. Both students must contribute equally to the assignment! Assignments must be submitted as Jupyter notebooks. The notebooks must run to completion in a reasonable amount of time.

Please refer to the schedule for homework release and due dates for more information

4. Final Project

There will be a final project during week 5 which will be graded. Students will work in groups of four and will select a topic from two advanced topics and accompanying references, provided beforehand. This implies that the students must learn and understand the new method, implement it in Python, and interpret the results in the context of the material learned during the duration of the course.

GRADING BREAKDOWN

  • Quiz/Exercises: 25%
  • Participation: 10%
    • Ed Forum: 5%
    • Lectures: 5%
  • Homework: 35%
  • Project: 20%

GRADING GUIDELINES

1. Quiz and Exercises

The quiz and exercises on the edStem platform will be graded based on the following:

  • How correct your answer to the questions are?
  • How many questions you have answered?

2. Homeworks

Homework will be graded based on the following:

  • How correct your code is;
    • Note: The Jupyter Notebook cells should run. We will not troubleshoot any part of the code.
  • How you have interpreted the results;
    • Note: Interpretation of results should be submitted as complete sentences. You will lose points if you only submit code.
  • How well you present the results (notebook readability, plots, and explanations).

Homework Grading Scale: 1-5

3. Project

The Final Project will be graded based on the following:

  • How well your code answers the problem at hand
    • Both the thought and efficiency of the solution is important.
  • How correct your code is;
    • Note: The Jupyter Notebook cells should run. We will not troubleshoot any part of the code.
  • How you have interpreted the results;
    • Note: Interpretation of results should be submitted as complete sentences. You will lose points if you only submit code.
  • How well you present the results (notebook readability, plots, and explanations). This plays an important role.

Homework Grading Scale: 1-5

COURSE POLICIES

Getting Help

For questions about homework, course content, package installation, after you have tried to troubleshoot yourselves, the process to get help is:

  • Post the questionon discourse and hopefully your peers will answer. Note that on discourse questions are visible to everyone. The teaching staff monitor the posts.
  • Attend Office Hours, this is the best way to get help.
  • For private matters send an email to the instructor.

Quoting Sources

You must acknowledge any source code that was not written by you by mentioning the original author(s) directly in your source code (comment or header). You can also acknowledge sources in a README.txt file if you used whole classes or libraries. Do not remove any original copyright notices and headers.

You are encouraged to use libraries, unless explicitly stated otherwise! You may use examples you find on the web as a starting point, provided their licenses allow you to re-use it. You must quote the source using proper citations (author, title, URL) both in the source code and in any publicly visible material.

Collaboration Policy

  • Each homework will be worked on and submitted in pairs. Make sure anyone in your team submits the homework.

  • While you are working on the homework, you can talk to anyone (even outside your group). This is encouraged.

  • Attribution is key!! Anything that you “borrow” from someone/someplace else please make sure you attribute and give necessary credits.

Deadlines and Late Days

Homeworks must be turned in on time. You can have upto 2 late days across the module, with no more than 1 late day on any homework. No late days on the final project.

Exceptions: No exceptions, except for illness, with a doctor’s certificate.

ACADEMIC HONESTY

Ethical behavior is an important trait, from ethically handling data to the attribution of code and work of others. Thus, in this session we give a strong emphasis to Academic Honesty.

As a student, your best guidelines are to be reasonable and fair. We encourage teamwork for problem sets, but you should not split the homework and you should work on all the problems together.

We have included some ideas below of acceptable and not acceptable behaviors. Engaging in not acceptable behavior regarding academic honesty will be handled accordingly.

Please be responsible and when in doubt ask the course instructors.

ACCEPTABLE:

  • Discussing materials and engaging in OH.
  • Helping debug.
  • Using a few lines of code found online or another forum as long as you cite the origin and attribute authorship of code.
  • Searching online to expand your knowledge and for debugging
  • Using a tutor, provided the tutor does not do your work for you.

NOT ACCEPTABLE:

  • Accessing a solution to some problem prior to submitting your own.
  • Failing to cite the origins of code or techniques that you discover outside of the course’s own lessons and integrate into your own work.
  • Paying or offering to pay an individual for work that you may submit as your own.
  • Providing or making available solutions to problem sets to individuals who might take this course in the future.
  • Searching for or soliciting outright solutions to problem sets online or elsewhere.
  • Splitting a problem set’s workload with another individual and combining your work.

DIVERSITY AND INCLUSION

We actively seek and welcome people of diverse identities, from across the spectrum of disciplines and methods since Artificial Intelligence (AI) increasingly mediates our social, cultural, economic, and political interactions [1].

We believe in creating and maintaining an inclusive learning environment where all members feel safe, respected, and capable of producing their best work.

We commit to an experience for all participants that is free from – Harassment, bullying, and discrimination which includes but is not limited to:

  • Offensive comments related to age, race, religion, creed, color, gender (including transgender/gender identity/gender expression), sexual orientation, medical condition, physical or intellectual disability, pregnancy, or medical conditions, national origin or ancestry.
  • Intimidation, personal attacks, harassment, unnecessary disruption of talks during any of the learning activities.

Reference:

[1] K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available: http://dx.doi.org/10.2139/ssrn.3428240.