Practical Artificial Intelligence Class Guide

Welcome to the practical AI class where innovative approaches to learning and teaching are prioritized to ensure that students not only understand theoretical concepts but are also capable of applying them in real-world scenarios. Below is a detailed guide on the structure and expectations of the class.

Group Division

• Students will be divided into groups at the beginning of the semester.
• Each group is responsible for working together to complete assignments and give presentations.

Assignments

There are three types of assignments in this class:

Weekly Assignments

• Overview: Weekly mini-assignments will be given to each group at the beginning of each week.
• Duration: One week.
• Presentation: Each week, a group member, who hasn't presented before, will present their work in a 10-15 minute presentation.
• Questioning: All members should be prepared to answer brief questions about the code and theoretical implementation.
• Deadline: Assignment should be submitted to supervisors on the night before presenting.
• Feedback and Revision: Supervisors will provide feedback on the code ( If the group give the assignment code before the deadline for fast review ), pointing out any forbidden libraries or block codes. Teams should revise and change the forbidden code/libraries before the presentation day. ( Using forbidden libraries or code will result in a grade decrease) Each member should be prepared to answer questions.

Final Assignment

• Overview: Assigned after the midterm.
• Duration: From after the midterm to the final practical exam day.
• Presentation and Grading: Similar to the other assignments.
• Deadline: Due on the final practical exam day.

Use of GitHub

Groups are advised to use GitHub for version control. Understanding and using GitHub CLI functionalities (pull, push, branches, comments, commits) is encouraged. Please do research about it and watch tutorials.

Use of Anaconda + Anaconda environments

Using Anaconda will be a great help for the members using its environments members could be synced to each other and use multiple Python versions for multiple projects. Please do research about it and watch tutorials.

Submissions

Members can submit their assignments by opening pull requests on GitHub by uploading files/folders that have the same camelcase name as their task titles

Using GitHub pull requests is highly recommended this will be good practice for the future to be familiar with how the company works and contribute open source libraries.

Pull request should contain any necessary information for the reviewers:

• project title
• group name
• presenter name ( student name who will present the project )

Folder Structure

Please apply following the folder structure as mentioned below.

week-[n]/
├─ Group [letter] - [project title]
│ ├─ requirements.txt
│ ├─ file 1
│ ├─ file 2
│ ├─ file 2
│ ├─ ..etc

You have to include your project Title, name and link as a comment in the top of your main.py

# Title: 1- RGB Color Detection
# any other information

Additional Notes

• Code Integrity: Groups should adhere to the guidelines regarding forbidden libraries and codes to ensure the integrity and learning - outcomes of the class.
• Collaboration and Participation: Active collaboration and participation are vital for the success of each group and individual learning.
• Time Management: Proper time management and adherence to deadlines are crucial for the smooth progress of the class and assignments.

In conclusion, this guide outlines the structure, assignment types, and expectations for the practical AI class. The unique approach, focusing on both practical and theoretical learning, aims to provide students with a comprehensive understanding and capability in AI, preparing them for real-world challenges and innovations.

Remember, even AI couldn’t learn everything in a day – and it doesn’t need coffee breaks!
Keep learning, keep coding, and may the forks (in your code) be with you!