Overview
The AI/ML: Foundations and Applications Bootcamp is an intensive training program designed
for advanced undergraduate and graduate students from the Department of Management Information Systems (MIS)
at the University of Dhaka. The bootcamp aims to build a strong foundation in artificial intelligence (AI)
and machine learning (ML), bridging theoretical understanding with hands-on practical skills.
Throughout the course, students will engage in real-world problem solving in domains such as cybersecurity,
health informatics, image processing, and natural language processing. They will gain hands-on experience
developing models using Python, Jupyter Notebook, and Google Colab, with a focus on the PyTorch framework.
By the end of the bootcamp, students will be equipped to design, analyze, and deploy AI/ML systems with
confidence.
Rules & Regulations
- Weekly assignments are mandatory and must be submitted via Canvas.
- In-class quizzes will be conducted every week via Canvas — often at the beginning of each
week’s session.
- Active participation in Microsoft Teams discussions is strongly encouraged.
- Late submissions will not be accepted unless prior permission is granted.
- Missing two sessions without prior notice will result in disqualification from the
bootcamp.
- Camera use is expected during live sessions unless you have a religious or other valid
exemption.
- Maintain a professional environment during class — minimize background noise and avoid
interruptions.
Honor Code and Academic Integrity
This bootcamp upholds the highest standards of academic integrity. Students are expected to conduct themselves with
honesty, transparency, and professionalism in all assignments, labs, discussions, and final projects. If you're ever
unsure whether something is allowed, please ask the course staff.
- OK to search, ask in public about the systems we’re studying.
- Always cite all the resources you reference (e.g., papers, online content, documentation).
- If you use an AI tool (e.g., ChatGPT), include a link to your search/chat history (e.g., a shared
ChatGPT workspace link) and explicitly mention this in your reports, labs, and final project.
- If you ask a question on a public forum such as Reddit or Stack Overflow, include the link to your post
in your submission.
- NOT OK to copy solutions from AI tools.
- Your submitted work must reflect your own understanding and effort.
- NOT OK to ask someone else to complete your assignments, labs, or projects.
- Academic integrity requires that all submitted work be your own.
- OK to discuss questions and ideas with classmates.
- Collaborative learning is encouraged, but you must disclose your discussion partners in your submission.
- NOT OK to copy solutions from classmates.
- All submitted work must be completed independently.
- OK to use existing solutions or implementations as part of your project or assignment.
- You must clearly cite and distinguish your own contributions from external sources.
- NOT OK to present someone else's solution as your own.
- Always attribute credit where it’s due, and clearly identify your original work.
- OK to publish your final project after the course is over.
- We encourage sharing your work publicly to contribute to the broader AI/ML community.
- NOT OK to post your assignment or lab solutions online.
- This helps preserve academic integrity for future participants and ensures fairness.
Reminder: Violating the honor code may result in removal from the bootcamp and disqualification from
receiving certification or awards. Our goal is to create a supportive, honest, and respectful learning environment
for everyone.
